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(a) Data partition sent by the provider Pa during the confidential data collection (step 9 of the protocol). (b) Data partition sent by the provider Pb during the confidential data collection (step 9 of the protocol). Quasi-identifier attributes are marked in bold. The asterisk identifies the masked attributes.
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Virtual learning environments contain valuable data about students that can be correlated and analyzed to optimize learning. Modern learning environments based on data mashups that collect and integrate data from multiple sources are relevant for learning analytics systems because they provide insights into students’ learning. However, data sets in...
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Electric vehicles (EVs) are becoming more popular due to environmental consciousness. The limited availability of charging stations (CSs), compared to the number of EVs on the road, has led to increased range anxiety and a higher frequency of CS queries during trips. Simultaneously, personal data use for analytics is growing at an unprecedented rat...
Citations
... In the AI-driven era, RDS practices are strongly recommended to ensure the responsible use of data while avoiding its pitfalls (e.g., data manipulation for business gains, discrimination about some minor sects, improper resource allocation to under-privileged people, etc.), and most prior methods have not undertaken what was said in the RDS. • We affirm the contributions of existing methods that identify QIDs from data and anonymize them in order not to lose usefulness and preserve privacy [16]. ...
Personal data have been increasingly used in data-driven applications to improve quality of life. However, privacy preservation of personal data while sharing it with analysts/ researchers has become an essential requirement to be met by data owners (hospitals, banks, insurance companies, etc.). The existing literature on privacy preservation does not precisely quantify the vulnerability of each item among user attributes, thereby leading to explicit privacy disclosures and poor data utility during published data analytics. In this work, we propose and implement an automated way of quantifying the vulnerability of each item among the attributes by using a machine learning (ML) technique to significantly preserve the privacy of users without degrading data utility. Our work can solve four technical problems in the privacy preservation field: optimization of the privacy-utility trade-off, privacy guarantees (i.e., safeguard against identity and sensitive information disclosures) in imbalanced data (or clusters), over-anonymization issues, and rectifying or enabling the applicability of prior privacy models when data have skewed distributions. The experiments were performed on two real-world benchmark datasets to prove the feasibility of the concept in practical scenarios. Compared with state-of-the-art (SOTA) methods, the proposed method effectively preserves the equilibrium between utility and privacy in the anonymized data. Furthermore, our method can significantly contribute towards responsible data science (extracting enclosed knowledge from data without violating subjects’ privacy) by controlling higher changes in data during its anonymization.
... This paper presents and applies on a dataset of higher education students, a protocol to mashup data and then anonymise it without losing the statistical usefulness of the data [20]. ...
... This protocol carries out the vertical integration of the data partitions identified in the setup protocol and the k-anonymisation of the aggregate quasi-identifier, which is built by vertically joining the quasiidentifier attributes of each partition. Privacy-preserving data collection and integration is achieved by decoupling the collection of quasi-identifiers from the collection of confidential data and by using what are known as privacy-preserving connectors (ppc) [20] -a pseudonym of that identifier attribute shared by all the vertical partitions. The ppc for a given record is computed as a collision-resistant hash function of the value that the identifier attribute holds in the record and a nonce common to all records. ...
The diversity of information sources available to educational institutions makes it necessary to mash up information in order to get the highest performance through learning analytics. Data mashup requires the implementation of data anonymisation methods in order to protect the privacy of the learners who appear in the data partitions. However, the process of anonymising this data mashup can lead to a loss of data utility. This paper presents a protocol for merging data mashups that preserves privacy by k-anonymising the data while preserving its analytical utility.
... It is transmitted like an envelope with an information message; WSDL clarifies the functional characteristics of the logical units that make up a specific w-out service. These specifications form the basis of the web service model, in which services, like ten components, turn the Internet into a huge distributed system [2][3] . ...
According to the problems of low resource utilization efficiency, single learning content and lack of personalization in e-learning system, a personalized e-learning system based on Web data mining is designed by applying web mining and ontology technology. The system can provide more satisfying teaching methods and learning resources according to the characteristic information of learners’ knowledge structure and learning preference, and create a relatively personalized e-learning environment. Experiments show that ontology technology can fully improve the mining effect, improve the management efficiency of learning resource database, effectively promote students’ network learning, meet students’ personalized learning needs, and provide intelligent auxiliary means for system decision analysis.
The authentication of users and devices is essential to the security of cyber-physical systems (CPS). But since various networks and devices are interconnected in CPS, they are vulnerable to cyberattacks, which can have detrimental effects on sectors like healthcare, IoT and blockchain technology. This paper highlights the difficulties faced by CPS in the healthcare system and stresses the value of security and privacy in safeguarding private medical information. The resource limitations, security level specifications, and system architecture of CPS-based healthcare systems, conventional security methodologies and cryptography solutions fall short. In order to better preserve and secure CPS in the healthcare industry, this paper investigates the possibilities of machine learning and multi-attribute feature selection. The suggested solution intends to address the drawbacks of traditional privacy preservation techniques and reduce concerns about sensitive information and data leakage. The security of healthcare data in CPS can be improved by utilizing machine learning techniques, which also aids in the creation of strong network security infrastructures for communication in healthcare applications.words.
