Geng Yang's research while affiliated with Nanjing University of Posts and Telecommunications and other places
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Publications (151)
Most of the traditional privacy-preserving search schemes adopt TF-IDF model which is on the basis of keyword frequency statistics. The embedding semantic association between keywords and documents are not considered. To solve this problem, we propose an efficient semantic-aware privacy-preserving multi-keyword search scheme over encrypted cloud da...
The growing concerns about data privacy in society lead to restrictions on the computer vision research gradually. Several collaboration-based vision learning methods have recently emerged,
e.g
., federated learning and split learning. These methods protect user data from leaving local devices, and make training performed only by uploading gradie...
In federated learning, multiple parties may use their data to cooperatively train a model without exchanging raw data. Federated learning protects the privacy of users to a certain extent. However, model parameters may still expose private information. Moreover, existing encrypted federated learning systems need a trusted third party to generate an...
Predicting and managing the movement of people in a region during epidemics’ outbreak is an important step in preventing outbreaks. The protection of user privacy during the outbreak has become a matter of public concern in recent years, yet deep learning models based on datasets collected from mobile devices may pose privacy and security issues. T...
Contact tracing, a transmission intervention, has shown effectiveness inpandemic control. However, most existing schemes only focus on direct contact. There has been little work on indirect contact. In addition,most schemes are too stereotypical to change the rules of “contact” flexibly. To address the above problems, this paper provides flexible c...
Currently, private data leakage and nonlinear classification are two challenges encountered in big data mining. In particular, few studies focus on these issues in support vector machines (SVMs). In this paper, to effectively solve them, we propose a novel framework based on the concepts of differential privacy (DP) and kernel functions. This frame...
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Cloud computing is now being used by more and more enterprises and individuals. To protect the privacy of outsourced data in the cloud, the searchable symmetric encryption is adopted. However, verifying search results to detect whether there is malicious behavior in the cloud server is still a challenge. In order to overcome this problem, we propos...
It is convenient to obtain enormous trajectory data by using the positioning chips equipped mobile devices, nowadays. The study of extracting moving patterns from trajectory data of moving objects is becoming a hot spot. Convoy is one of the popular studied patterns, which refers to a group of objects moving together for a period of time. The exist...
Traditional term frequency‐inverse document frequency model‐based privacy‐preserving ranked search schemes rarely consider the latent semantic meanings of documents and keywords. It is a challenge to design efficient semantic‐aware ranked search (SRSE) schemes with privacy preservation. In this paper, two privacy‐preserving SRSE schemes are develop...
Granting users precise access rights is one of the purposes of access control technologies. With the increasing requirements of fine-grained authorization, too strict or too loose access control models may cause many problems. In this paper, aiming at insufficient authorizations in text databases, we propose a risk-aware topic-based access control...
Traditional searchable encryption schemes construct document vectors based on the term frequency-inverse document frequency (TF-IDF) model. Such vectors are not only high-dimensional and sparse but also ignore the semantic information of the documents. The Sentence Bidirectional Encoder Representations from Transformers (SBERT) model can be used to...
In federated learning (FL), each client collaboratively trains the global model through the cloud server (CS) without sharing its original dataset in edge computing. However, CS can analyze and forge the uploaded parameters and infer the privacy of clients, which calls for the necessity of verifying the integrity and protecting the privacy for aggr...
Insufficient authorization and overauthorization are two main problems to be solved in access control systems. If the authorization is too strict, users might not be able to access data that should be accessible. If the authorization is too lax, users might obtain too many access rights, which may cause considerable risks. Finer-grained access cont...
Federated learning (FL) can protect clients’ privacy from leakage in distributed machine learning. Applying federated learning to edge computing can protect the privacy of edge clients and benefit edge computing. Nevertheless, eavesdroppers can analyze the parameter information to specify clients’ private information and model features. And it is d...
The proliferation of various items recommended by Internet-based systems has resulted in the exponential growth of the number of ratings in the big data era. Recent advances in matrix factorization have made it an effective way to process these ratings for recommendations. However, we confront a challenge in deploying a matrix factorization model f...
For data analysis with differential privacy, an analysis task usually requires multiple queries to complete, and the total budget needs to be divided into different parts and allocated to each query. However, at present, the budget allocation in differential privacy lacks efficient and general allocation strategies, and most of the research tends t...
Data privacy threat arises during providing top-k query processing in the wireless sensor networks. This article presents an efficient privacy-preserving and collusion-resisting top-k(EPCT) query processing protocol. A minimized candidate encrypted dataset determination model is first designed, which is the foundation of EPCT. The model guides the...
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 encryp...
Now, many application services based on location data have brought a lot of convenience to people’s daily life. However, publishing location data may divulge individual sensitive information. Because the location records about location data may be discrete in the database, some existing privacy protection schemes are difficult to protect location d...
In big data era, massive and high-dimensional data is produced at all times, increasing the difficulty of analyzing and protecting data. In this paper, in order to realize dimensionality reduction and privacy protection of data, principal component analysis (PCA) and differential privacy (DP) are combined to handle these data. Moreover, support vec...
Radio resource slicing is critical to customize service provisioning in fifth-generation (5G) uplink radio access networks (RANs). Using drone-small-cells (DSCs) as aerial support for terrestrial base stations can enhance the flexibility for resource provisioning in response to traffic distribution variations. In this paper, we study a multi-DSC-as...
In the era of Industry4.0, cloud-assisted industrial control system (ICS) is considered to be the most promising technology for industrial processing automation systems. However, the emerging attack techniques targeted at ICS underlines the importance of data security. To protect the data from the unauthorized accesses, attribute-based encryption i...
As an emerging and efficient paradigm for multimedia systems, fog networking has attracted widespread attention over the last few years. However, an increasing number of attacks in the current virtualized environments underlines the importance of secure data sharing. Unfortunately, existing multimedia data sharing schemes are not suitable for the n...
Traditional searchable encryption schemes based on the Term Frequency-Inverse Document Frequency (TF-IDF) model adopt the presence of keywords to measure the relevance of documents to queries, which ignores the latent semantic meanings that are concealed in the context. Latent Dirichlet Allocation (LDA) topic model can be utilized for modeling the...
The improvements in location-acquisition technologies make massive trajectory data available. Discovering objects that move together over trajectory data is beneficial in many applications. In this paper, a novel concept called loose tracking behavior is proposed to investigate the problem of detecting objects that travel together with a target. We...
With the advance of cloud computing technology, increasingly more documents are encrypted before being outsourced to the cloud for great convenience and economic savings. Thus, how to design a fast and accurate multi-keyword ranked search scheme over encrypted cloud data is of paramount importance. In this article, we propose a fast and accurate se...
Cloud has become one of the most widely used technologies to store data due to its availability, flexibility, and low cost. At the same time, the security, integrity, and privacy of data that needs to be stored on the cloud is the primary threat for cloud deployment. However, the increase in cloud utilization often results in the creation of a mult...
Cloud computing technology has revolutionized the field of data management as it has enhanced the barriers of storage restrictions and high-cost establishment for its users. The benefits of the cloud have paved the way for its extensive implementation in large enterprises. However, the data in the cloud have succumbed to various security threats, a...
As bitcoin has drawn a lot of attention, people have developed various cryptocurrencies based on blockchain framework. The decentralization feature of blockchain makes the transaction information public in the cryptocurrency. It is possible to expose the user’s privacy. Therefore, it is necessary to design an efficient and secure scheme to hide tra...
Unstructured data (mostly text data) have become a vital part in the era of big data. Hence, it has become increasingly difficult to identify the internal relations among data and describing the access control object during the design of access control (especially fine-grained access control) policies. Furthermore, in recent years, security inciden...
In the interest of privacy concerns, cloud service users choose to encrypt their personal data before outsourcing them to cloud. However, it is difficult to achieve efficient search over encrypted cloud data. Therefore, how to design an efficient and accurate search scheme over large-scale encrypted cloud data is a challenge. In this paper, we inte...
In cloud computing, data owners outsource their data to clouds for saving cost of data storage and computation. However, while enjoying the benefits of cloud computing, users have to face the risk that sensitive outsourced data could be leaked. This paper proposes a privacy-preserving multi-keyword ranked search scheme over encrypted cloud data, wh...
Currently, searchable encryption has attracted considerable attention in the field of cloud computing. The existing research mainly focuses on keyword-based search schemes, most of which support the exact matching of keywords. However, keyword-based search schemes ignore spelling errors and semantic expansions of keywords. The significant drawback...
Although current proposed compression schemes achieve better performance than traditional data compression schemes, they have not fully exploited the spatial and temporal correlations among the data, and the design of the projection (measurement) matrix cannot satisfy the requirement of real scenarios adaptively. Hence, well-designed clustering alg...
With the rapid development of cloud computing services, more and more individuals and enterprises prefer to outsource their data or computing to clouds. In order to preserve data privacy, the data should be encrypted before outsourcing and it is a challenge to perform searches over encrypted data. In this paper, we propose a privacy-preserving mult...
With the continuous upgrading of smart devices, people are using smartphones more and more frequently. People not only browse the information they need on the Internet, but also more and more people get daily necessities through online shopping. Faced with a variety of recommendation systems, it becomes more and more difficult for people to keep th...
With the widespread application of big data, privacy-preserving data analysis has become a topic of increasing significance. The current research studies mainly focus on privacy-preserving classification and regression. However, principal component analysis (PCA) is also an effective data analysis method which can be used to reduce the data dimensi...
Traditional searchable encryption schemes adopting the bag-of-words model occupy massive space to store the document set’s index, where the dimension of the document vector is equal to the scale of the dictionary. The bag-of-words model also ignores the semantic information between keywords and documents, which could return non-relevant search resu...
With searchable encryptions in the cloud computing, users can outsource their sensitive data in ciphertext to the cloud that provides efficient and privacy-preserving multi-keyword top-k searches. However, most existing top-k search schemes over encrypted cloud data are the centralize schemes which are limited in large scale data environment. To su...
Traditional searchable encryption schemes mostly adopt the TF-IDF (term frequency - inverse document frequency) model which ignores the semantic association between keywords and documents. It is a challenge to design an effective and secure semantic-aware search scheme. The topic model is based on “high-order co-occurrence”, i.e., how often words c...
Graph embedding-based learning methods have been widely employed to reduce the dimensionality of high-dimensional data, while how to construct adjacency graphs to discover the essential structure of the data is the key problem in these methods. In this paper, we present a novel algorithm called graph discriminant embedding (GDE) for feature extract...
In recent years, attribute-based access control (ABAC) models have been widely used in big data and cloud computing. However, with the growing importance of data content, using data content to assist authorization for access controls has become more common. In this paper, we propose a dynamic content-driven attribute-based access control model (CAB...
Due to the convenience, economy and high scalability of cloud computing, more and more individuals and enterprises are motivated to outsource their data or computing to clouds. In this paper, we propose a privacy-preserving multi-keyword ranked search over encrypted data in hybrid cloud, which is denoted as MRSE-HC. The keyword partition vector mod...
Cyber physical system (CPS) is facing enormous security challenges because of open and interconnected network and the interaction between cyber components and physical components, the development of cyber physical systems is constrained by security and privacy threats. A feasible solution is to combine the fully homomorphic encryption (FHE) techniq...
This paper aims to analyze the technology of chatbots and investigate its development, which is becoming a popular trend now. A chatbot can simulate a human being to interact with the people in real-time, using the natural language and sends its response from a knowledge base and a set of business rules. Firstly, by using a few examples of the famo...
The existing public key-based en-route filtering schemes are vulnerable to report disruption attacks or selective forwarding attacks, and they fail to consider any measure to detect and punish the malicious nodes. The authors propose a series of public key-based security mechanisms for wireless sensor networks (WSNs) in this paper, including a mech...
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Outsourcing data in clouds is adopted by more and more companies and individuals due to the profits from data sharing and parallel, elastic, and on-demand computing. However, it forces data owners to lose control of their own data, which causes privacy-preserving problems on sensitive data. Sorting is a common operation in many areas, such as machi...
Many basic scientific works use Wireless Sensor Networks (WSNs) to collect environmental data and use the observations for scientific research. The completeness and accuracy of the collected environmental observations determine the reliability of the research results. However, due to the inherent characteristics of WSNs, data loss and data error us...
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To reduce the energy waste caused by idle listening, sensor nodes in wireless sensor networks (WSNs) usually work with low-duty-cycle mode. However, such mode brings many new challenges, especially for broadcasting applications. This paper proposes to exploit the broadcast nature of wireless media to further save energy for broadcasting in low-duty...
The issue of the privacy-preserving of information has become more prominent, especially regarding the privacy-preserving problem in a cloud environment. Homomorphic encryption can be operated directly on the ciphertext; this encryption provides a new method for privacy-preserving. However, we face a challenge in understanding how to construct a pr...
With the advance of database-as-a-service (DaaS) and cloud computing, increasingly more data owners are motivated to outsource their data to cloud database for great convenience and economic savings. Many encryption schemes have been proposed to process SQL queries over encrypted data in the database. In order to obtain the desired data, the SQL qu...
Data classification is a fundamental problem in many research areas. This paper proposes a novel classifier, namely local mean representation based classifier (LMRC), for data classification. Based on the concept that neighboring samples should have most similar properties and for a testing sample, the similar properties should be concentrated on t...
This paper introduces a novel dimensionality reduction algorithm, called collaborative representation based local discriminant projection (CRLDP), for feature extraction. CRLDP utilizes collaborative representation relationships among samples to construct adjacency graphs. Different from most graph-based algorithms which manually construct the adja...
Locality-regularized linear regression classification (LLRC) is an effective classifier that shows great potential for face recognition. However, the original feature space cannot guarantee the classification efficiency of LLRC. To alleviate this problem, we propose a novel dimensionality reduction method called locality-regularized linear regressi...
In wireless sensor networks, secure MAX/MIN query processing is a challenging issue, and it is useful in fields where security is necessary. In this paper, we propose a secure MAX/MIN query processing method in two-tiered wireless sensor networks (TWSN). To the best of our knowledge, it is the first work that can achieve data privacy protection and...
In order to protect data privacy whilst allowing efficient access to data in multi-nodes cloud environments, a parallel homomorphic encryption (PHE) scheme is proposed based on the additive homomorphism of the Paillier encryption algorithm. In this paper we propose a PHE algorithm, in which plaintext is divided into several blocks and blocks are en...
Network management, planning, and optimization rely on accurate and complete traffic measurement. However, anomalies and missing data are inevitable in direct traffic measurement because of high measurement cost and unreliable network transport protocol etc. Existing traffic matrix estimation approaches only concern the outlier, but ignore the stru...
How to capture distinctive features from facial images when there are large variations in illumination, poses, and expressions is important for the face recognition problems. This paper introduces a novel algorithm called fuzzy linear regression discriminant projection (FLRDP) for face recognition. The proposed algorithm FLRDP seeks to generate an...
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In the field of wireless sensor networks, the secure range query technique is a challenging issue. In two-tiered wireless sensor networks, a verifiable privacy-preserving range query processing method is proposed that is based on bucket partition, information identity authentication, and check-code fusion. During the data collection process, each s...
Deployed in some unattended environment, the nodes of wireless sensor networks (WSNs) could be easily captured by the adversaries to become malicious nodes due to lack of appropriate protective measures. Therefore, how to protect the reliability of data transmission become an important issue in WSNs. In this paper, we mainly study the reliable data...
Data gathering is one of the most important operations in many wireless sensor networks (WSNs) applications. In order to implement data gathering, a tree structure rooted at the sink is usually defined. In most wireless sensor networks, nodes are powered by batteries with limited energy. Prolonging network lifetime is a critical issue for WSNs. As...
Linear discriminant regression classification (LDRC) was presented recently in order to boost the effectiveness of linear regression classification (LRC). LDRC aims to find a subspace for LRC where LRC can achieve a high discrimination for classification. As a discriminant analysis algorithm, however, LDRC considers an equal importance of each trai...
In recent years, we have seen many applications of secure query in two-tiered wireless sensor networks. Storage nodes are responsible for storing data from nearby sensor nodes and answering queries from Sink. It is critical to protect data security from a compromised storage node. In this paper, the Communication-efficient Secure Range Query (CSRQ)...
Privacy-preserving data queries for wireless sensor networks (WSNs) have drawn much attention recently. This paper proposes a privacy-preserving MAX/MIN query processing approach based on random secure comparator selection in two-tiered sensor network, which is denoted by RSCS-PMQ. The secret comparison model is built on the basis of the secure com...
