Results 51 to 60 of about 12,439,211 (191)
K-Anonymous Privacy Preserving Manifold Learning
Proceedings of the 20th International Conference on Security and Cryptography, 2023 In this modern world of digitalization, abundant amount of data is being generated. This often leads to data of high dimension, making data points far-away from each other. Such data may contain confidential information and must be protected from disclosure. Preserving privacy of this high-dimensional data is still a challenging problem.
Garg, Sonakshi, Torra, Vicenç
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Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases
Algorithms, 2018 In the context of this research work, we studied the problem of privacy preserving on spatiotemporal databases. In particular, we investigated the k-anonymity of mobile users based on real trajectory data.
Elias Dritsas +3 more
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Efficient Multidimensional Suppression for K-Anonymity [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2010 Many applications that employ data mining techniques involve mining data that include private and sensitive information about the subjects. One way to enable effective data mining while preserving privacy is to anonymize the data set that includes private information about subjects before being released for data mining. One way to anonymize data set is
Kisilevich, Slava +3 more
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θ-Sensitive k-Anonymity: An Anonymization Model for IoT based Electronic Health Records
Electronics, 2020 The Internet of Things (IoT) is an exponentially growing emerging technology, which is implemented in the digitization of Electronic Health Records (EHR).
Razaullah Khan +7 more
semanticscholar +1 more source
Trajectory Clustering and k-NN for Robust Privacy Preserving k-NN Query Processing in GeoSpark
Algorithms, 2020 Privacy Preserving and Anonymity have gained significant concern from the big data perspective. We have the view that the forthcoming frameworks and theories will establish several solutions for privacy protection.
Elias Dritsas +5 more
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MULTIPLE RELEASES OF k-ANONYMOUS DATA SETS AND k-ANONYMOUS RELATIONAL DATABASES
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2012 In data privacy, the evaluation of the disclosure risk has to take into account the fact that several releases of the same or similar information about a population are common. In this paper we discuss this issue within the scope of k-anonymity. We also show how this issue is related to the publication of privacy protected databases that consist of ...
Stokes, Klara, Torra, Vicenç
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IEEE Access, 2020 With the gradually opening of energy markets and popularization of Electric Vehicles (EVs), EVs can transmit, dispatch and recharge energy in different markets and domains dynamically.
Yangyang Long +4 more
semanticscholar +1 more source
A Differentially Private (Random) Decision Tree without Noise from k-Anonymity
Applied SciencesThis paper focuses on the relationship between decision trees, a typical machine learning method, and data anonymization. It is known that information leaked from trained decision trees can be evaluated using well-studied data anonymization techniques ...
Atsushi Waseda, Ryo Nojima, Lihua Wang
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k-Anonymity Location Privacy Algorithm Based on Clustering
IEEE Access, 2018 The accuracy of user location information is inversely proportional to the user's privacy preserving degree k, and is proportional to quality of query service.
Lijuan Zheng +5 more
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Sensors, 2023 With the popularity of location services and the widespread use of trajectory data, trajectory privacy protection has become a popular research area. k-anonymity technology is a common method for achieving privacy-preserved trajectory publishing.
Hua Shen, Yu Wang, Mingwu Zhang
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