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Achieving Efficient Cloud Search Services: Multi-Keyword Ranked Search over Encrypted Cloud Data Supporting Parallel Computing
Abstract
Cloud computing is becoming increasingly popular. A large number of data are outsourced to the cloud by data owners motivated to access the large-scale computing resources and economic savings. To protect data privacy, the sensitive data should be encrypted by the data owner before outsourcing, which makes the traditional and efficient plaintext keyword search technique useless. So how to design an efficient, in the two aspects of accuracy and efficiency, searchable encryption scheme over encrypted cloud data is a very challenging task. In this paper, for the first time, we propose a practical, efficient, and flexible searchable encryption scheme which supports both multi-keyword ranked search and parallel search. To support multi-keyword search and result relevance ranking, we adopt Vector Space Model (VSM) to build the searchable index to achieve accurate search results. To improve search efficiency, we design a tree-based index structure which supports parallel search to take advantage of the powerful computing capacity and resources of the cloud server. With our designed parallel search algorithm, the search efficiency is well improved. We propose two secure searchable encryption schemes to meet different privacy requirements in two threat models. Extensive experiments on the real-world dataset validate our analysis and show that our proposed solution is very efficient and effective in supporting multi-keyword ranked parallel searches.
... R. Salakhutdinov, and A. Mnih, [1] contemplate on "Probabilistic lattice factorization related rate forecast is explained.X. Yang, H. Steck, also Y. Liu, [2] clarifies that "Circle-based suggestion in online interpersonal organizations this works is procedures of web based life related information expectation and score, " M. Jiang, P. Cui, R. Liu, Q. Yang, F. Wang, W. Zhu, and S. Yang, [3] "Social relevant information forecast and proposal," M. Jamalias well as M. Ester, [4] "A lattice factorization method with trust engendering for proposal in informal communities accomplishes great outcomes ," Z. Fu, X. Sun, Q. Liu, et al [5] "Accomplishing Efficient Cloud Search Services: Multi-Keyword Ranked Search over Encrypted Cloud Data Supporting Parallel Computing positive and negative degree words idea," G. Ganu, N. Elhadad, A Marian, [6] "Past the stars: Improving rating expectations utilizing star rating ,Review content substance" J. Xu, X. Zheng, W. Ding, [7] "Customized suggestion dependent on audits and appraisals mitigating the sparsity issue of collective sifting got successful and exact outcomes X. Qian, H. Feng, G. Zhao, and T. Mei, [8] "Customized proposal joining client intrigue and group of friends, similar to twitter watts application and so on" H. Feng, and X. Qian, [9] "Suggestion by means of client's character and social relevant information is anylized Z. Huang, X. Li, F. Wu, [19] Review rating expectation dependent on the substance and weighting solid social connection of commentators," F. Li, N. Liu, H. Jin, K. Zhao, Q. Yang, X. Zh [20], "Consolidating analyst and item data for audit rating forecast. Estimation investigation is led on various coatings of content. ...
... Such layers be situated expression founded, sentence founded, audit based. Survey [1], [2], [7], [8] also sentence [5] put together coatings are worked with respect to entire content to endeavor grouping dependent on predefined extremity, for example, positive, negative, unbiased on entire content without a moment's delay. Then again expression based layer [13] investigation endeavors to separate the assessment extremity regarding the matter talked about in the content. ...
We've experienced a bulk of review pages such as Amazon, Flip Kart & Facebook, book my show and some applications etc., in latest studies. It offers an excellent chance to share our reviews for different goods we buy. However, we are faced with the issue of duplicating data. It is essential where to stock precious data from feedback to comprehend the desires of a user and create a precise suggestion. Some variables, such as user buy documents, item type, and geographic place, are considered by conventional recommendation schemes. We suggest a Feeling Forecast Technique (FFT) in this job to enhance forecast precision in recommendation schemes. First, we suggest a personal assessment strategy for personal consumers and determine the feeling about objects/products of each customer. Second, we regard not only the own emotional characteristics of a customer but also the relational emotional impact. After which they count the credibility of the item that could be deduced from a consumer sets emotional scores reflecting the extensive assessment of clients. Finally, we combine in our recommendation scheme three elements feeling resemblance relational emotional, impact, and notoriety resemblance of item to create a precise forecast of rating. On an actual-world sample obtained from Glass door, we undertake a quality assessment of the three emotional variables. Our findings show that the feeling can well describe display settings that help improve the effectiveness of recommendations. Above all discussion is analyzed with ML based Naive Bayes optimization got 20% more efficiency compared to existed methods like linear regression, etc.
... In most of the existing cipher-text sorting retrieval methods, KNN (K Nearest Neighbor) technology is used to create indexes supporting cipher-text retrieval [3]- [5]. In the process of massive data encryption search, most of the search encryption schemes have high time complexity and large storage space, which are closely related to the encrypted key, the document index and query request dimensions [6], [7]. ...
... v numbers are randomly selected from (t + 1) to (t + u) to generate a set V . The value of the j−th(j = 1, 2, · · · , t+u+1) dimension of the query requestQ is assigned according to (5). ...
With the continuous improvement of the security of cloud storage, more users upload private data to the cloud. However, a large number of encrypted data using independent keywords to create indexes not only directly increase the storage overhead, but also lead to the decline of search efficiency. Therefore, this paper proposes an efficient search method using features to match joint keywords (FMJK) on encrypted cloud data. This method proposes that each
$d$
keywords are randomly selected from the non-duplicated keywords, which are extracted from the documents of the data owner, to generate a joint keyword, and all joint keywords form a keyword dictionary. Each joint keyword is matched with the feature of the document and the query keyword respectively, and the result obtained by the former is regarded as a dimension of the document index, while the result obtained by the latter is regarded as a dimension of the query trapdoor. Finally, the BM25 algorithm is used to calculate the inner product of the document index and the trapdoor, and then sort them and the top k results are returned. Theoretical analysis and experimental results show that the proposed method is more feasible and more effective than the compared schemes.
... Further, the cloud executes the search and give back the results to the user. Inspired by this framework, many schemes were proposed based on "symmetric searchable encryption (SSE) (Fu et al. 2015;Xia et al. 2015;Ding et al. 2019;Shahien et al. 2021) and public-key searchable encryption (PSE) (Wan and Deng 2016;Zhang et al. 2018;Noroozi and Eslami 2020)". Unfortunately, these SSE and PSE methods do not uphold access control. ...
... Later, many SE schemes were proposed by different authors to include different functionalities. Fu et al. (2015) presented a scheme supporting search with multiple keywords. The scheme is based on SSE and uses a vector space model as an index structure. ...
Attribute-based searchable encryption (ABSE) allows only authorized users to perform a keyword search over encrypted data in the cloud while preserving the data privacy and keyword privacy. Although ABSE provides data privacy, access control, and keyword search, it does not support data authenticity which plays a major role in the cloud environment to ensure that the data is not modified. Alongside, improving search efficiency in ABSE becomes mandatory since the cloud is attracting massive data. To address these issues, in this paper, we propose a practical verifiable multi-keyword attribute-based searchable signcryption scheme in cloud storage. The scheme uses ciphertext-policy attribute-based signcryption to achieve data privacy, access control, and data authenticity. Further, we integrate the multi-dimensional B+\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$B^{+}$$\end{document}-tree with the Merkle tree in index construction to enhance the search efficiency and to verify the search results. The security analysis proves that our scheme satisfies security requirements such as data privacy and authenticity, index and query privacy, trapdoor unlinkability. We also prove that our scheme is secure against chosen plaintext attacks and signature forgery attacks. Finally, the performance analysis demonstrates that the proposed scheme is efficient and practical.
... Semantic based scheme was extended for mobile [98]. MT scheme with similarity-based ranking used A tree-based index structure [25], and various adaption methods for multidimensional (MD) algorithms are proposed to improve search efficiency [17]. ...
... The simulation results demonstrated that their scheme was less suitable for practical usage with moderate overhead. However, in their work, the terms are closely related to the files, which could lead to a potential information leak Fu et al. [66] proposed an efficient solution for supporting ranked keyword search problems. In this technique, the single random keyword is the input to the cloud server, and the cloud server generates the most related file that matches the input keyword. ...
Security has become a significant concern with the increased popularity of cloud storage services. It comes with the vulnerability of being accessed by third parties. Security is one of the major hurdles in the cloud server for the user when the user data that reside in local storage is outsourced to the cloud. It has given rise to security concerns involved in data confidentiality even after the deletion of data from cloud storage. Though, it raises a serious problem when the encrypted data needs to be shared with more people than the data owner initially designated. However, searching on encrypted data is a fundamental issue in cloud storage. The method of searching over encrypted data represents a significant challenge in the cloud. Searchable encryption allows a cloud server to conduct a search over encrypted data on behalf of the data users without learning the underlying plaintexts. While many academic SE schemes show provable security, they usually expose some query information, making them less practical, weak in usability, and challenging to deploy. Also, sharing encrypted data with other authorized users must provide each document's secret key. However, this way has many limitations due to the difficulty of key management and distribution. We have designed the system using the existing cryptographic approaches, ensuring the search on encrypted data over the cloud. The primary focus of our proposed model is to ensure user privacy and security through a less computationally intensive, user-friendly system with a trusted third party entity. To demonstrate our proposed model, we have implemented a web application called CryptoSearch as an overlay system on top of a well-known cloud storage domain. It exhibits secure search on encrypted data with no compromise to the user-friendliness and the scheme's functional performance in real-world applications.
... Adapting the designed protocol through the distributed authentication of erased coded data and the system performs public validate and data dynamics in opposition to third-party valuators that illustrates the finding of data fraud throughout verification of storage space accuracy on the distributed servers. The protocol presents the some security and activities assurance: Public audit ability, Storage correction, Confidentiality protecting, Light and Batch audit [19]. The propose model aspires to secure the cloud data from unreliable service suppliers. ...
The cloud storage with user facilities like great data storage quality, higher computing, scalable and flexible has been one of the major application of cloud computing. Large number of data holders is subcontracting the files to the cloud. The cloud server being a public domain is not very reliable thus the data holders are required to find a reliable and trustworthy way to check the ownership of the data files that they subcontract on the cloud server which is present in a remote location. To tackle this drawback number of Distant Data Integrity Checking (DDIC) protocols is in the literature but these protocols have liabilities with respect to the data dynamics and the efficiency. This work recommends a novel DDIC protocol built across homomorphic-hash-function. This system gives a resistance against number of attacks like replay, replaces and falsifications. This work introduces the Operation-record-table(ORT) which is an optimized table resulting in keeping the constant cost for supporting the data variations and keep tracks of blocking the file operations.
... In the research of privacy protection, a key issue is to achieve privacy security while ensuring data usability. In 2014 and 2015, Krontiris and Fu et al. [10,11] proposed to minimize the reduction of information or achieve the goal of individual anonymity, which can be achieved by building an integrated architecture system based on actual sharing behavior and combining technical solutions. The well-known k-anonymity privacy protection model proposed by Samarati and Sweeney [12,13] successfully forbade privacy leakage and avoided the possibility for multi-owners to be identified only by a unique identification. ...
With the rapid development of social networks, personal privacy leakage has become more and more serious. A social network is a shared platform. Resources in a social network may be shared by multiple owners. In order to prevent privacy leakage, each owner assigns a corresponding privacy protection strategy. For the same shared contents, integrating the privacy protection strategies of all owners is the key problem for sharing. This paper proposes a rule fusion method of privacy protection for the co-ownership of data shared in social networks. First, the content of the protection is defined according to different privacy requirements. Second, this paper uses predicate logic formulas to abstract the natural language-based description of privacy protection and further provides a logical model of privacy protection rules. Third, this paper gives the definition of privacy protection heterogeneous rules and provides a rule fusion algorithm to ensure no conflict exists among these rules. The experimental results show that the proposed rule-based fusion method of privacy protection strategy performs at a higher level than the privacy protection strategy fusion.
... Secure authentication protocols are being developed, and researchers have studied the security weaknesses of those protocols [5], [6], [7]. Preserving the confidentiality of the data encryption is an effective technology [10], [11], [12]. ...
... Paper Review: Achieving Efficient Cloud Search Services: Multi-Keyword Ranked Search over Encrypted Cloud Data Supporting Parallel Computing [7] Overview: ...
Search engines play an essential role in our daily lives. Nonetheless, they are also very crucial in enterprise domain to access documents from various information sources. Since traditional search systems index the documents mainly by looking at the frequency of the occurring words in these documents, they are barely able to support natural language search, but rather keyword search. It seems that keyword based search will not be sufficient for enterprise data which is growing extremely fast. Thus, enterprise search becomes increasingly critical in corporate domain. In this report, we present an overview of the state-of-the-art technologies in literature for three main purposes: i) to increase the retrieval performance of a search engine, ii) to deploy a search platform to a cloud environment, and iii) to select the best terms in expanding queries for achieving even a higher retrieval performance as well as to provide good query suggestions to its users for a better user experience.
... A variety of applications, including keyword-based data restoration and image duplication detection, have been suggested for the cloud recently. Xia et al. [34] presented a novel tree-based document architecture and a dynamic, two-keyword method to enable updated data after reevaluation of [35] suggested an adjustable transportable cryptography arrange that incorporates multi-keyword looked for and identical investigation. In their multi-keyword fuzzy pursuit study, Fu et al. [16] explored another method for dealing with the errors that arise throughout the placement phase and presented a useful research question. ...
As data processing advances, decentralized media has been widely recognized for its ability to store large amounts of data. By comparing a revisited content to a dispersed repository, a cloud provider may verify the document’s integrity without having to retrieve it. A reconsidered examining strategy is offered to lead the customer to reconsider the significant assessing task to third sector inspector, taking into consideration the important computing cost brought up by the checking process (TPA). TPA may be deterred by the primary revisited evaluating strategy, but the second plan gives the harmful organization the right of inspection over the readdressed data of users, which poses a significant risk to patient privacy. Human Emphasis for reconsidered inspection is presented in this work, which emphasizes that the service user can be overwhelmed by her own data. Based on user-centered design, our suggested methodology not only prevents patient’s data from leaking to TPA without depending on cryptographic algorithms, but can also avoid the use of additional free unpredictable supply that is impossible to fulfill on a daily basis. Also, we start to make our approach work with continuous changes. Our recommended scheme is both verifiably safe and essentially productive, as shown by the privacy analysis and test evaluations.
... The application of computer technology in physical education also consciously penetrates the scientific and technological consciousness and imparts scientific and technological knowledge to the students. It can be seen that modern computer technology has a good application value in sports training model design [3]. ...
In order to improve the visual level of physical training and promote the optimization of sports training, the computer multimedia technology and Internet of things (IoT) are used to simulate the sports training model. Traditionally, the design complexity and control performance of the high-dimensional spatial sports training model are high, and the control performance is poor. A design method of computer multimedia simulation sports training model is proposed based on attitude change space fast exploration control, and computer multimedia simulation method is adopted to construct human body sports training and body movement mathematical model. In the physical training and kinematics model of human body, the position and pose feature information of human body is extracted quickly. Multimedia image analysis and intelligent control are used to simulate the process of human sports training and analyze the constraint parameters of motion planning in attitude change space to realize the optimal control of physical training and body motion planning. The simulation results show that the design of sports training model based on computer multimedia simulation is more controllable, the prediction error of motion attitude parameter is lower, and the multimedia control ability of sports training model is stronger, the effect of physical training is improved. In addition, the simulation results verify the effectiveness of in IoT.
... In [17], a scheme is proposed to protect and search cloud data, supporting the dynamic operations on documents. The scheme of [18] exploits the computing performance of the cloud server, proposing a scheme to support not only keyword sorting but also parallel search. ...
Traditional centralized cloud services face challenges, such as high communication delay, single point of failure, etc. Edge computing is one of distributed computing services in which resources are placed closer to users, and thus is more robust and can reduce communication delay. Although outsourcing data to the edge servers can bring great convenience, it also brings serious security threats. In order to provide image retrieval while ensuring users’ data privacy, a privacy-preserving content-based image retrieval scheme in edge environment is proposed. Considering the distributed characteristics of edge environment and the requirement of lightweight computing, we design a secure interaction protocol completed through the collaborative cooperation of two or more edge servers. This scheme not only protects data privacy, but also has low communication overhead and delay as well as low computing complexity, therefore it is more suitable for edge computing environment. Theoretical analysis and experiments show that our scheme has high security, retrieval accuracy and efficiency.
This chapter presents the beginnings of searchable encryption, definitions, and types. Also, one section is focused on the components of searchable encryption schemes, presenting the architectures, security requirements, and search operation types. Further, the chapter presents recent advancements in searchable encryption discussing the advantages, disadvantages, performance, security requirements, and presents case studies and practical applications in different domains (for example, Internet-of-Things, Machine Learning, Artificial Intelligence, etc.). Lastly, the chapter presents the challenges and research directions for searchable encryption.
For a utility company to reduce
CO2 emissions along with managing load demands, it must strive for a 100 percent renewable electrical power generation. Europe takes initiatives to achieve this goal by developing a super smart grid (SSG) based on renewable energy resources (RERs) by 2050. The SSG is based on two exclusive alternatives: wide area and decentralized power generation using large number of RERs. Before developing such SSG, there is a need to address the critical issues associated with RERs, i.e., load flow balancing and transients stability. Considering a reliability issue involved with RERs and the random deviations between demand response and generation response patterns, load flow balancing and transient stability become challenging research issues in SSGs. These technical issues are also considered to be more challenging, if an unexpected outage in the form of an occurrence of three phase (L-L-L) faults (TPF) arises in SSGs, due to power quality disturbances. To address this problem, load flow balancing probabilistic modeling is performed in this research paper in order to formulate the complexity of randomness between generation and demand response patterns through transmission network planning (TNP) in the form of a super smart node (SSN) transmission network infrastructure. Moreover, a further optimization in SSN transmission network has been done with the addition of a cooperative control strategies in terms of an integrating vehicle to grid (V2G) technology in SSN transmission network in order to achieve further enhancement in load flow balancing and transient stability in SSGs. Furthermore, as SSGs power infrastructure is based on different clusters, therefore in order to accommodate various clusters for load flow balancing, a continuous spinning reserve (CSRs) probabilistic modeling has also been performed in this paper in terms of its integration in SSN transmission network. Considering above probabilistic analysis, future contingencies are easily predictable, before any kind of disruptive changes arises in a SSGs due an occurrence of a TPF. Moreover, from simulation results as performed in this paper, we can easily verified that our proposed probabilistic algorithm of load flow balancing and transients stability outperforms existing literature work and can also achieved near optimal performance, even for a broad range of variations in load and also in case of an arising of significant power quality disturbances in SSGs due to an occurrence of TPF.
The demand for memory capacity has increased, and cloud energy usage has soared. The performance and scalability of virtualization interfaces in cloud computing are hampered by a lack of sufficient memory. To figure out this problem, a technique defined as memory deduplication is widely used to reduce memory consumption utilizing the page-sharing method. However, this method of memory deduplication using KSM has significant drawbacks, such as overhead owing to many online comparisons, which will consume so many CPU resources. In this research, a modified approach of Memory Deduplication of Static Memory Pages (mSMD), which is based on the identification of similar applications by Fuzzy hashing and clustering them using the Hierarchical Agglomerative Clustering approach, followed by similarity detection between static memory pages based on Genetic Algorithm and details stored in Multilevel shared page table, both operations performed in offline and final memory deduplication is carried out during online, is proposed for achieving performance optimization in virtual machines by reducing memory capacity requirements. When compared to existing techniques, the simulation results indicate that the proposed approach mSMD efficaciously minimizes the memory capacity required while improving performance.
The Identity based plan gives proficient dynamic data operations to data in distributed computing. This is on the grounds that client wishes to do different block level operation on the data document by assuring the data integrity. It accept that CSS will give the right data to client without deceiving the client. The block Level operation performed in fine grained updates. To accomplish this, this plan uses an adaptable data segmentation strategy and a data auditing convention. The data segmentation is the method for splitting the entire document into countable number of parts and are put away in various server areas. This strategy is improved the situation data security. The enemy does not know the document areas of different divided parts of record. Subsequently, he can't see the entire gathered single record. In this way we can secure the data. In the mean time, it address a potential security issue in supporting open unquestionable status to make the plan more ensured and strong, which is accomplished by adding an extra authorization process among the three partaking gatherings of customer, server and a Manager. For better security, our plot incorporates an extra authorization process with the point of eradicating dangers of unapproved review difficulties from pernicious or imagined outsider inspectors, which we term as 'approved auditing'. Subsequently, the portioned records are encoded and put away in various server areas for enhancing the security purposes.
In this chapter, we focus on introducing and discussing event-triggered distributed subgradient algorithms for solving a class of convex optimization problems for first-order discrete-time multi-node systems based on undirected networks. In such algorithms, the communication process of the entire network is controlled by a set of trigger conditions monitored by each node. Each node’s trigger condition and event-triggered distributed subgradient optimization algorithm are entirely decentralized and simply rest on the local objective functions and individual states of each node and its neighbors at the event-triggered sequence of themselves. At each time instant, each node updates its state at the time instant of its respective event trigger by using its own objective function and the state collected from itself and its neighboring nodes. Under the condition that the topology of the undirected network is connected and the design parameters are appropriate, we will establish a sufficient condition to ensure consensus and reach an optimal solution. We will finally give a theoretical analysis to show that the event-triggered distributed subgradient algorithm is able to steer the nodes of the entire network to converge asymptotically to the optimal solution of the convex optimization problem. Finally, simulation results are used to validate the effectiveness of the algorithm and to demonstrate the feasibility of the theoretical analysis.
Because of the rapid growth of cloud computing and the expansion of mobile phone users in recent years, mobile cloud computing has attracted wide attention. In the mobile cloud, wireless computing networks are the basics of sharing data between mobile devices and cloud services. Since air is the communication medium, it must be properly protected; otherwise, it will be subject to a variety of security threats, for example, attacks from middle-man, identity tracking, etc... Furthermore, mobile devices are limited in storage, resources, and computing powers. Hence, designing an efficient and secure balance of authentication schemes is extremely important. First of all, a multi-factor authentication scheme based on biometric (fingerprint information), hash function, and fuzzy vault algorithm is presented in that paper. Secondly, the Validation and Analysis tool of AVISPA Security was approved. Thirdly, the security of the scheme proposed is compared to other related schemes.
With the progress of the times and the development of electronic products, more and more images are captured and stored on multiple cloud servers, which are more likely to exhibit dishonesty because of interdependence rather than semi-honesty and thus how to achieve search over multiple clouds becomes intractable. Moreover, most of the existing research works on content-based image retrieval so far do not consider dishonest clouds and thereby do not support multi-cloud retrieval. On the one hand, a central search server is generally introduced for the convenience of retrieval in the system with multiple cloud servers. However, the central search server may return false or incomplete search results for possible commercial profit, which reduces the retrieval accuracy. On the other hand, cloud servers may destroy the image integrity by deleting or tampering images to save computing and bandwidth resources, resulting in the loss of important information for the image owner and incorrect decryption of the image for the user. In order to address these two problems, we propose a blockchain-assisted privacy-protected similarity image retrieval in the multiple cloud environment, which can not only ensure the accuracy of the retrieval results, but also can verify the integrity of images. The performance of this scheme is evaluated on a real-world dataset, and the results show the high retrieval precision and efficiency of this scheme.
Users outsource images to edge servers physically closer to their location for real time applications because of the low latency and low transmission overhead. Outsourcing to these edge servers however, increases the risks to data privacy. Almost all existing privacy preserving image retrieval schemes utilize a single cloud server to execute retrieval tasks and provide centralized image retrieval but at high computational costs, thus are not suitable for the distributed edge environments with limited computing resources. We propose a lightweight privacy-preserving multi-source image retrieval scheme adapted specifically for the distributed edge environment. We apply high efficiency orthogonal decomposition and learning with errors (LWE) strategy to encrypt image features and construct cipher indexes and trapdoors, guaranteeing the security of the data, while reducing computational costs. The orthogonality of data ensures that the accuracy of retrieval results is not compromised by the random numbers used in the scheme. In addition, the proxy re-encryption technology is adopted to support the retrieval of multi-source images encrypted by unique data owners with different keys. A detailed performance analysis and comprehensive experiments demonstrate that our scheme guarantees data security with very high retrieval accuracy and a low computational burden, consistent with the demands of edge environments.
In this article, an analytical and numerical analysis of a computer virus epidemic model is presented. To more thoroughly examine the dynamics of the virus, the classical model is transformed into a fractional order model. The Caputo differential operator is applied to achieve this. The Jacobian approach is employed to investigate the model's stability. To investigate the model's numerical solution, a hybridized numerical scheme called the Grunwald Letnikov nonstandard finite difference (GL-NSFD) scheme is created. Some essential characteristics of the population model are scrutinized, including positivity boundedness and scheme stability. The aforementioned features are validated using test cases and computer simulations. The mathematical graphs are all detailed. It is also investigated how the fundamental reproduction number R 0 functions in stability analysis and illness dynamics. 2338
Regarding the permanent-magnet synchronous generator (PMSG)-based wind turbine system, this paper proposes a modified flux-coupling-type superconducting fault current limiter (SFCL) to enhance its fault ride-through (FRT) performance. The modified SFCL's structural principle and theoretical influence on the PMSG ride through capability are conducted, and a comparison of the SFCL and a dynamic braking chopper (BC) is performed. Using MATLAB, a detailed model of a 1.5-MW PMSG-based wind turbine integrated with the SFCL/BC is built, and the simulations of symmetrical and asymmetrical faults are done. From the results, introducing the modified SFCL can limit the fault currents in the generator and grid sides. In addition, using the modified SFCL, we are able to compensate the generator voltage and alleviate the dc-link overvoltage. Thus, the wind turbine system's power balance is improved, and also the fault recovery process can be accelerated. On the whole, the modified SFCL is better than the BC for assisting the FRT operation of the PMSG.
The Internet of Medical Things (IoMT) is an important application of the Internet of Things in health care. In IoMT, efficiency and user privacy are crucial for cloud storage and retrieval of healthcare data documents. Existing schemes, however, often suffer from inefficient retrieval and increased risk of privacy disclosure when dealing with massive data. We propose here a new Efficient Encrypted Parallel Ranking (EEPR) search system, block-based and privacy-preserved, for encrypted cloud healthcare data. We design a parallel binary search tree structure in block and propose a parallel retrieval algorithm adaptable to such a structure. A quantitative analysis through the information retention index shows that our scheme demonstrates better search performance. In addition, feature vectors generated from our scheme are difficult to be reversely analyzed due to unexplainability, enhancing privacy protection for patients and researchers. A formal security analysis shows that our EEPR scheme is resistable to known background attack, and yields a lower time complexity and significantly improves search efficiency as well as accuracy over existing schemes.
At present, the healthcare business is implementing new bits of knowledge hastily. Mostly, the Data Skill, which is castoff to support surgeons and patients identical individually, and to advance the conveyance of healthcare amenities. The entire vital segment of an infirmary info association currently is located in the Electronic Health Record (EHR), where patient data is stored. Moreover, an uncountable number of claims are cast off by the infirmary supervised to display the therapeutic ability's concert in rapports of fiscal effectiveness and handling triumph tariffs. In accumulation to this, regime and centralized system of government as well use IT resolutions to crisscross the eminence and protection of healthcare associations. Furthermore, patients practice several fitness intensive care applications and strategies to monitor their vitals and to interconnect with surgeons over portable and tuner devices. These days as supercomputers have turned out to be a crucial portion of our day‐to‐day subsistence, it is progressively significant that information safety has similarly positioned obverse and center on our tilt of significances. Primarily in the healthcare business, wherever views are frequently attentive on redeemable somebody's lifespan and accurately, therefore, but safeguarding admittance to boundaries and mainframe structures that hoard private information like therapeutic histories is similarly a critical aspect to reflect. Information safety is a conforming deed amid monitoring admittance to data while permitting unrestricted and informal admittance to individuals who want it. One more significant motive is the feeble fortification of patient's information in health organizations. Economic organizations like tiers partake previously formed robust prearrangement of information safety. The two‐factor verification has to turn out to be a widespread usual for levels. The level permits its customer admittance to the data solitary afterwards entering the One‐Time Codeword. Nonetheless, on the opposite, in communal fitness connotations, such organizations have not actually instigated in a stretched period, and therefore they develop an open quarry for the replicated offenders.
The conditions of villages in India have improved the last few decades; however, people are migrating from village to rural towns where there are insufficient healthcare facilities. The need now is to create “smart villages” and “smart rural towns”. In developing countries have high incidences of neonatal, infant, child mortality, and malnutrition. Governments, aid agencies, and NGOs have taken a range of steps over time to minimize child mortality, infant mortality, hunger, and maternal mortality. In addition to the governments initiatives across different schemes, science and technology must be harnessed for their solution. Physical and intellectual development is affected by malnutrition among children. In the long term, malnutrition impacts overall production and the countrys economic growth. Neonatal, child mortality, and malnutrition suggest that adequate maternal care raises birth weight to minimize infants' low birth weight. Their maternal health development feature explains that it is necessary to have an early onset of prenatal care and a minimum number of prenatal care visits to improve both mothers and unborn childrens health.
Ensuring the privacy of data outsourced to the cloud, while also ensuring data usability, remains challenging. In this article, we propose a global secure ranked multikeyword search scheme based on multiple data owners in cloud-based systems. Compared to prior arts, our scheme improves search precision and efficiency and eliminates keyword redundancies. To improve search efficiency, we use a tree-based index structure, where we remove the need to search all related files. To improve search precision, we design a global computing scheme in which we compute the relevance score in a global manner with the global document frequency. To reduce the redundancies of keywords, we design a keyword conversion scheme, where we re-encrypt keywords with a third entity. As a result, the same keyword extracted by different data owners will be encrypted into the same form. Also, we present the security analysis and demonstrate the performance of the scheme with experiments.
This paper proposes a novel CNN-based image tampering detection and self-recovery scheme, this method focus on protecting digital images from forensic manipulation. For this purpose, the watermark algorithm should have two main features first, detecting the tampered zone of the received image by using an authentication watermark; second, recovering the tampered zone by utilizing an image digest watermark. For tamper detection, we propose the CNN-based authentication watermark generation. The image digest is generated using a DCT transform and a jpeg quantization table. “An End-to-End Compression Framework Based on CNN” is used to compress the image digest and improve the quality of recovering the tampered area. We code the compressed image digest by Reed-Solomon error correction code to protect it from a high tampering ratio. Finally, we use Arnold transform for embedding the authentication watermark and image digest watermark in the host image. This embedding method improves security issues. To show the proposed scheme's performance, conduct many experiments. The precision and recall score are calculated to evaluate tampering detection, while for evaluation of image self-recovery, calculate the PSNR and SSIM values of the recovered image. The comparisons with the state-of-the-art methods show that the proposed scheme is superior in imperceptibility, security, and recovery capability.
In the “Internet + Education” mode, a big data analysis of innovative talents education model is conducted to improve the quantitative evaluation ability of innovative talent education and training, and the “Internet + Education” innovative talent education model under the background of big data is proposed based on segmented information fusion and regression statistical analysis. More and more attention is attracted by the continuous advancement of innovative talent training models. The resource data mining and information processing research of innovative talent education models are conducted in related literature and have achieved certain research results. A big data analysis model for the cultivation of innovative talents is constructed and a structured big data information reorganization method is adopted to conduct information fusion processing of the “Internet + Education” innovative talents education model. Features describing the associated information of the talent cultivation model are extracted and the segmented information fusion method is adopted for feature clustering processing. The autoregression analysis of innovative talents cultivation evaluation ability is conducted based on feature clustering results and a test statistic model is constructed for effective analysis of the “Internet + Education” innovative talents education model under big data background. As a result, comprehensive and fuzzy decision‐making on the talent education model is realized according to judgment statistics. Simulation results show that the accuracy of a quantitative assessment of the “Internet + Education” innovative talents education model is higher with this method and the convergence of regression analysis is better. This shows that this method can effectively instruct the innovation of the talent education model and novel technologies can be more utilized by the efficiency and performance of the method.
Cloud computing has gained attention due to its sophisticated processing architecture and data storage capabilities in the last couple of years. Due to high volume data and the endless number of possible users, the security of the account holder's stored data and privacy becomes essential for this paradigm. This article focuses on the encryption architecture of data storage and retrieval by creating an encrypted searchable index, which is inspired by symmetric searchable encryption. Ranking becomes a need for providing the best out of the search results to the user. This research article proposes an efficient and flexible artificial neural network (ANN) based ranking scheme to search for documents from the cloud server. The proposed algorithm architecture is segmented into three parts. The first part is the generation of the encryption index over the uploaded data, the second part is query analysis, and the third part is ranking. To consolidate the encryption mechanism, RSA, NTRU, and AES were used based on the requirement of the data. To orient the retrieval part, the degree of the top keyword in the server is determined by using term frequency with inverse document frequency schemes. The retrieved documents are further ranked using ANNs. The simulation setup was done on MATLAB 2016b having datasets from Kaggle (Twitter data) and FIRE dataset.
The article first introduces the specific steps of a quantum-secure direct communication scheme that sends a single photon at a time. Based on the basic steps of the scheme, it is gradually extended to a quantum secure direct communication scheme that transmits single-photon sequences in two and four times, with emphasis on the coding rules corresponding to each scheme. The purpose is that through the above scheme, it can be intuitively seen in the subsequent efficiency analysis that with the increase of the number of transmissions, the classification of single photons can be increased, and the encoding capacity of each single photon and the transmission efficiency of quantum states in the entire communication can be greatly improved. Finally, a universal scheme and coding rules for quantum secure direct communication by sending single photons in integer powers of 2 are proposed, and the scheme is proved to be safe and feasible after security analysis. Through the efficiency analysis, the communication efficiency of this scheme is higher than that of the existing scheme, and the implementation of this scheme only uses a single photon, does not involve quantum entanglement, and has more application value.
We explore the field of searchable encryption (SE) and present a comprehensive survey of relevant literature. Since the existing survey articles are primarily written for the security experts, we present the discussion to assist the general practitioners (not from security background) in identifying an appropriate SE scheme for their application of interest. We initiate with the brief overview of SE along with its application-oriented criteria. By analyzing various SE schemes, we derive five significant characteristics – key structure, search structure, search functionality, support to reader/writers, and reader’s capability. Based on these characteristics, we categorize the existing SE schemes and showcase the significant features offered by each scheme. We explore numerous schemes based on symmetric/asymmetric key structures, simple/inverted search structure, single/multi-keyword search functionality, single/multiple reader/writer support, and verification functionality owned by data reader. A most promising part of the survey is the comparative analysis of the existing schemes under specific category in terms of tables showing efficiency and security. We hope that this survey is indeed beneficial for the general practitioners to pick an appropriate SE scheme better suited to the selected application.
Secure and reliable sensing plays the key role for cognitive tracking i.e., activity identification and cognitive monitoring of every individual. Over the last years there has been an increasing
interest from both academia and industry in cognitive authentication also known as biometric recognition. These are an effect of individuals’ biological and physiological traits. Among various traditional
biometric and physiological features, we include cognitive/brainwaves via electroencephalogram
(EEG) which function as a unique performance indicator due to its reliable, flexible, and unique
trait resulting in why it is hard for an un-authorized entity(ies) to breach the boundaries by stealing
or mimicking them. Conventional security and privacy techniques in the medical domain are not
the potential candidates to simultaneously provide both security and energy efficiency. Therefore,
state-of-the art biometrics methods (i.e., machine learning, deep learning, etc.) their applications
with novel solutions are investigated and recommended. The experimental setup considers EEG
data analysis and interpretation of BCI. The key purpose of this setup is to reduce the number of
electrodes and hence the computational power of the Random Forest (RF) classifier while testing EEG
data. The performance of the random forest classifier was based on EEG datasets for 20 subjects. We
found that the total number of occurred events revealed 96.1% precision in terms of chosen events.
Keywords: cognitive authentication; IoT; healthcare; EEG; biometrics; sensing
With the growing demands of online access to health services, multiple medical institutions store their encrypted electronic health records (EHR) on the cloud and share them with the specified doctors and researchers. Multi-key searchable encryption (MKSE) is very suitable for this case for the sake of the privacy of patients and the system effeciency. In MKSE, multiple data owners can encrypt the data with their own secret keys and upload them to the remote cloud server. In order to search all the encrypted data on the cloud for a keyword, an authorized user only needs to generate a single trapdoor whose length is independent on the number of the data owners. MKSE allows multiple data owners to share their data with users efficiently. In this paper, we present an efficient MKSE scheme which supports fine-grained access control and conjunctive keyword searches. Both the control policy and keyword expressivity of our scheme are more flexible than the existing MKSE schemes. Meanwhile, our scheme can resist the keyword guessing attack. We will simulate our MKSE scheme and show that it is practical in the real world applications.
This chapter introduces security threats, security requirements and common techniques of security protection in IoT processing layer. A typical IoT processing layer is cloud computing, hence the security of cloud computing can represent the IoT processing layer security. Cloud computing security includes cloud computing platform security, security of cloud computing services, and data security in cloud computing environment. As in other information systems, access control techniques are described as part of IoT processing layer security. Cloud computing platform also uses the technique of virtual computing, and the security mechanism of virtual computing is introduced.
With the comprehensive development of cloud computing technology, more and more enterprises and individuals tend to outsource computing, data, and other resources to the cloud service providers to save the data management cost. Since the plaintext data outsourcing in the cloud could leak users private information, it is highly recommended to encrypt them before outsourcing. However, it is a challenge to perform searches over encrypted cloud data. In this paper, we adopt the keyword grouping idea into the traditional inverted index and propose a keyword-grouping inverted index (KGI-index). Based on the index, we propose a privacy-preserving KGI-index based multi-keywords ranked search scheme (KMRS). To improve the search efficiency, we adopt two strategies including grouping high relevant keywords and using the complete binary tree structure to optimize the index. The security analysis and experimental result show that the proposed scheme is a privacy-preserving and efficient multi-keyword ranked search scheme over encrypted cloud data.
Searchable encryption provides an effective way for data security and privacy in cloud storage. Users can retrieve encrypted data in the cloud under the premise of protecting their own data security and privacy. However, most of the current content-based retrieval schemes do not contain enough semantic information of the article and cannot fully reflect the semantic information of the text. In this paper, we propose two secure and semantic retrieval schemes based on BERT (bidirectional encoder representations from transformers) named SSRB-1, SSRB-2. By training the documents with BERT, the keyword vector is generated to contain more semantic information of the documents, which improves the accuracy of retrieval and makes the retrieval result more consistent with the user’s intention. Finally, through testing on real data sets, it is shown that both of our solutions are feasible and effective.
The public sector organizations are increasingly interested in using data science and capabilities of artificial intelligence (AI) to engender administrative and support services. The AI is identified for deepening the scientific knowledge in extents of administration, support services and addressing public queries. Other than private sector, the public services are focusing towards the advancement of technology through the arrangement of AI. Correspondingly, this chapter strives to view the artificial intelligence within the domains of governance while intervening in public services and localities during COVID-19. It considers the factors that the study endorses with opportunities and challenges that disclose the prospects in the current scenario. The study aims to highlight the use of AI in governance and support services through the online survey from the local departments and administrative service providers. The scope addressed in the study included the key state divisions, trailed by the National Health and Services, Science and Technology, Relief and Welfare departments, along with the key stakeholders in localities. The study examines the facilitative governance, shared objectives, imparting information, communication, socializing, AI expertise, and decision making over coronavirus governance. The results reflect a comprehensive strategy through artificial intelligence application to minimize the devastation caused by the event of coronavirus. It also invites public and private parties to support the government in technological affairs of governing the crisis situation of COVID-19. The research contributed for further investigation in the tools that can better the state governance services towards common citizen of Pakistan. Significant future implications are noted in view of the challenges and opportunities received and incorporated in the study.
This book focuses on the implementation of Artificial Intelligence in Business, Education and Healthcare, It includes research articles and expository papers on the applications of Artificial Intelligence on Decision Making, Entrepreneurship, Social Media, Healthcare, Education, Public Sector, FinTech, and RegTech. It also discusses the role of Artificial Intelligence in the current COVID-19 pandemic, in the health sector, education, and others. It also discusses the impact of Artificial Intelligence on decision-making in vital sectors of the economy.
In the Fourth Industrial Revolution, recent technologies progress in the spaces of Artificial Intelligence (AI) are changing entrepreneurship and economic growth significantly. Recent research found high levels of concern about how AI impacts the productivity of entrepreneurship and economic growth. This chapter aims to develop a theoretical framework for thinking about AI’s potential implications on the relationship between entrepreneurship and economic growth. First, we model entrepreneurship in new start-ups as the principal cause of business cycles and economic growth. Then, drawing from the Knowledge Spillover Theory, we suggest that AI can bring the benefit of creating new knowledge to entrepreneurship and thus contribute to economic growth. Finally, we address the possible main lines of future developments for AI, entrepreneurship and economic growth.
This chapter discussed the importance of enterprise resource planning (ERP) in different organization and how it will enhance productivity for the workers and for the patients by providing best services using innovative technologies. Also, this research will define the impact of innovative technologies in organization productivity with ERP system. Moreover, this chapter determine dependent which is organization productivity and independent variables such as RFID, telemedicine, mobility, artificial intelligence and innovative technologies which can be integrated with ERP system and it will help in improving organization productivity to describe the best use of ERP system to the healthcare organization when adopting innovative technology. As a result, implementing innovative technologies within healthcare organizations it will benefit the patients and physicians working in organization. Hence, the critical role of IT department is to determine the overall success in organizations and provide flexible, economical services to physicians, patients and end users involved in the organization.
Bahrain has witnessed many key developments in several sectors. The role of the national drive has contributed to economic diversification and financial thrive. Bahrain considers itself a getaway to the entire world due to its strategic location and ideal environment that attracts many startups and business entities. Recently, the government of Bahrain exhibits an outstanding potential in Artificial Intelligence (AI) and started to tap into the digital transformation era due to the Fourth Industrial Revolution (4th IR). To be aligned with the Bahrain Economic Vision 2030, the financial sector regulated by the Bahrain Central Bank (CBB) implemented several financial solutions based on innovation in technology to increase the customer experience and satisfaction which ultimately brings economic fruitful results. Moreover, Bahrain becomes the second world hub for Islamic Finance services after Malaysia with more than 25 Islamic Finance organizations. Furthermore, it undertakes major milestone to adapt the concept of artificial intelligence in the financial and banking sector and has created a competitive regional presence in Financial Technology (FinTech). This research aims to briefly define the notion of artificial intelligence (AI) and highlight Bahrain's achievements in the same field during the Fourth Industrial Revolution and how the artificial intelligence contributes to a rapid development even though it is still in its early phases. Beside this, the research presents the women empowerment in Bahrain and their active participation in all arenas particularly in the financial sector with exceptional lead in innovation. One of the advanced technologies arises in Bahrain is the (Fintech) which promotes the financial services to a superior level. Therefore, artificial intelligence investments require certain set of knowledge and skills to be obtained by the workforce that led to excellent conclusions. Based on the academic perceptions, the main probable challenges of AI are addressed. A set of recommendations are proposed to overcome the uncertainty and accomplish the planned vision 2030.
This paper aims to clarify the Fourth Industrial Revolution (IR 4.0) in the emergence of artificial intelligence (AI) and its various forms. Besides explaining intellectual capital (IC), its methods of measurement, and its effect on AI development. On the other hand, this paper clarified the role of AI that helped early disclosure of the COVID-19 pandemic and the preventative detection that was taken with the help of AI, and that helped contain the pandemic with the help of AI. As expected, this will lead to an increased interest in IC and work to develop AI, especially robots and digital transactions that will be dependent on technology 5G, which will impact the habits and traditions in the present and future of the COVID-19 pandemic.
With the growing popularity of cloud computing, huge amount of documents are outsourced to the cloud for reduced management cost and ease of access. Although encryption helps protecting user data confidentiality, it leaves the well-functioning yet practically-efficient secure search functions over encrypted data a challenging problem. In this paper, we present a verifiable privacy-preserving multi-keyword text search (MTS) scheme with similarity-based ranking to address this problem. To support multi-keyword search and search result ranking, we propose to build the search index based on term frequency- and the vector space model with cosine similarity measure to achieve higher search result accuracy. To improve the search efficiency, we propose a tree-based index structure and various adaptive methods for multi-dimensional (MD) algorithm so that the practical search efficiency is much better than that of linear search. To further enhance the search privacy, we propose two secure index schemes to meet the stringent privacy requirements under strong threat models, i.e., known ciphertext model and known background model. In addition, we devise a scheme upon the proposed index tree structure to enable authenticity check over the returned search results. Finally, we demonstrate the effectiveness and efficiency of the proposed schemes through extensive experimental evaluation.
Privacy-preserving document exchange among collaboration groups in an enterprise as well as across enterprises requires techniques for sharing and search of access-controlled information through largely untrusted servers. In these settings search systems need to provide confidentiality guarantees for shared information while offering IR properties comparable to the ordinary search engines. Top-k is a standard IR technique which enables fast query execution on very large indexes and makes systems highly scalable. However, indexing access-controlled information for top-k retrieval is a challenging task due to the sensitivity of the term statistics used for ranking. In this paper we present Zerber +R – a ranking model which allows for privacy-preserving top-k retrieval from an outsourced inverted index. We propose a relevance score transformation function which makes relevance scores of different terms indistinguishable, such that even if stored on an untrusted server they do not reveal information about the indexed data. Experiments on two real-world data sets show that Zerber +R makes economical usage of bandwidth and offers retrieval properties comparable with an ordinary inverted index.
Service providers like Google and Amazon are moving into the SaaS (Software as a Service) business. They turn their huge infrastructure into a cloud-computing environment and aggressively recruit businesses to run applications on their platforms. To enforce security and privacy on such a service model, we need to protect the data running on the platform. Unfortunately, traditional encryption methods that aim at providing "unbreakable" protection are often not adequate because they do not support the execution of applications such as database queries on the encrypted data. In this paper we discuss the general problem of secure computation on an encrypted database and propose a SCONEDB Secure Computation ON an Encrypted DataBase) model, which captures the execution and security requirements. As a case study, we focus on the problem of k-nearest neighbor (kNN) computation on an encrypted database. We develop a new asymmetric scalar-product-preserving encryption (ASPE) that preserves a special type of scalar product. We use APSE to construct two secure schemes that support kNN computation on encrypted data; each of these schemes is shown to resist practical attacks of a different background knowledge level, at a different overhead cost. Extensive performance studies are carried out to evaluate the overhead and the efficiency of the schemes.
Cloud computing economically enables the paradigm of data service outsourcing. However, to protect data privacy, sensitive cloud data has to be encrypted before outsourced to the commercial public cloud, which makes effective data utilization service a very challenging task. Although traditional searchable encryption techniques allow users to securely search over encrypted data through keywords, they support only Boolean search and are not yet sufficient to meet the effective data utilization need that is inherently demanded by large number of users and huge amount of data files in cloud. In this paper, we define and solve the problem of secure ranked keyword search over encrypted cloud data. Ranked search greatly enhances system usability by enabling search result relevance ranking instead of sending undifferentiated results, and further ensures the file retrieval accuracy. Specifically, we explore the statistical measure approach, i.e. relevance score, from information retrieval to build a secure searchable index, and develop a one-to-many order-preserving mapping technique to properly protect those sensitive score information. The resulting design is able to facilitate efficient server-side ranking without losing keyword privacy. Thorough analysis shows that our proposed solution enjoys "as-strong-as-possible" security guarantee compared to previous searchable encryption schemes, while correctly realizing the goal of ranked keyword search. Extensive experimental results demonstrate the efficiency of the proposed solution.
We study the setting in which a user stores encrypted documents (e.g. e-mails) on an untrusted server. In order to retrieve documents satisfying a certain search criterion, the user gives the server a capability that allows the server to identify exactly those documents. Work in this area has largely focused on search criteria consisting of a single keyword. If the user is actually interested in documents containing each of several keywords (conjunctive keyword search) the user must either give the server capabilities for each of the keywords individually and rely on an intersection calculation (by either the server or the user) to determine the correct set of documents, or alternatively, the user may store additional information on the server to facilitate such searches. Neither solution is desirable; the former enables the server to learn which documents match each individual keyword of the conjunctive search and the latter results in exponential storage if the user allows for searches on every set of keywords.
We define a security model for conjunctive keyword search over encrypted data and present the first schemes for conducting such searches securely. We propose first a scheme for which the communication cost is linear in the number of documents, but that cost can be incurred “offline” before the conjunctive query is asked. The security of this scheme relies on the Decisional Diffie-Hellman (DDH) assumption. We propose a second scheme whose communication cost is on the order of the number of keyword fields and whose security relies on a new hardness assumption.
There is a vast body of work on implementing anonymous communication. In this paper, we study the possibility of using anonymous communication as a building block, and show that one can leverage on anonymity in a variety of cryptographic contexts. Our results go in two directions. middot Feasibility. We show that anonymous communication over insecure channels can be used to implement unconditionally secure point-to-point channels, broadcast, and general multi-party protocols that remain unconditionally secure as long as less than half of the players are maliciously corrupted. middot Efficiency. We show that anonymous channels can yield substantial efficiency improvements for several natural secure computation tasks. In particular, we present the first solution to the problem of private information retrieval (PIR) which can handle multiple users while being close to optimal with respect to both communication and computation
With the advent of cloud computing, data owners are motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. But for protecting data privacy, sensitive data has to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of paramount importance. Considering the large number of data users and documents in the cloud, it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to these keywords. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the search results. In this paper, for the first time, we define and solve the challenging problem of privacy- preserving multi-keyword ranked search over encrypted cloud data (MRSE). We establish a set of strict privacy requirements for such a secure cloud data utilization system. Among various multi- keyword semantics, we choose the efficient similarity measure of "coordinate matching", i.e., as many matches as possible, to capture the relevance of data documents to the search query. We further use "inner product similarity" to quantitatively evaluate such similarity measure. We first propose a basic idea for the MRSE based on secure inner product computation, and then give two significantly improved MRSE schemes to achieve various stringent privacy requirements in two different threat models. Thorough analysis investigating privacy and efficiency guarantees of proposed schemes is given. Experiments on the real-world dataset further show proposed schemes indeed introduce low overhead on computation and communication.
Searchable encryption is a technique that allows a client to store documents on a server in encrypted form. Stored documents
can be retrieved selectively while revealing as little information as possible to the server. In the symmetric searchable
encryption domain, the storage and the retrieval are performed by the same client. Most conventional searchable encryption
schemes suffer from two disadvantages. First, searching the stored documents takes time linear in the size of the database,
and/or uses heavy arithmetic operations. Secondly, the existing schemes do not consider adaptive attackers; a search-query
will reveal information even about documents stored in the future. If they do consider this, it is at a significant cost to
the performance of updates. In this paper we propose a novel symmetric searchable encryption scheme that offers searching
at constant time in the number of unique keywords stored on the server. We present two variants of the basic scheme which
differ in the efficiency of search and storage. We show how each scheme could be used in a personal health record system.
Searchable symmetric encryption (SSE) allows a party to outsource the storage of his data to another party in a private manner, while maintaining the ability to selectively search over it. This problem has been the focus of active research and several security definitions and constructions have been proposed. In this paper we review existing security definitions, pointing out their short- comings, and propose two new stronger definitions which we prove equivalent. We then present two constructions that we show secure under our new definitions. Interestingly, in addition to satisfying stronger security guarantees, our constructions are more ecient than all previous constructions. Further, prior work on SSE only considered the setting where only the owner of the data is capable of submitting search queries. We consider the natural extension where an arbitrary group of parties other than the owner can submit search queries. We formally define SSE in this multi-user setting, and present an ecient construction.
It is desirable to store data on data storage servers such as mail servers and file servers in encrypted form to reduce security and privacy risks. But this usually implies that one has to sacrifice functionality for security. For example, if a client wishes to retrieve only documents containing certain words, it was not previously known how to let the data storage server perform the search and answer the query, without loss of data confidentiality. We describe our cryptographic schemes for the problem of searching on encrypted data and provide proofs of security for the resulting crypto systems. Our techniques have a number of crucial advantages. They are provably secure: they provide provable secrecy for encryption, in the sense that the untrusted server cannot learn anything about the plaintext when only given the ciphertext; they provide query isolation for searches, meaning that the untrusted server cannot learn anything more about the plaintext than the search result; they provide controlled searching, so that the untrusted server cannot search for an arbitrary word without the user's authorization; they also support hidden queries, so that the user may ask the untrusted server to search for a secret word without revealing the word to the server. The algorithms presented are simple, fast (for a document of length n, the encryption and search algorithms only need O(n) stream cipher and block cipher operations), and introduce almost no space and communication overhead, and hence are practical to use today