Journal ArticleDOI
Entropy-Based Quantification of Privacy Attained Through User Profile Similarity
Priti Jagwani,Saroj Kaushik +1 more
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TLDR
This work quantifies the amount of privacy gain attained through K anonymity technique of location privacy, opting-for users with similar profiles instead of random users, by using KL divergence.Abstract:
Location-based services refer to services that use location as primary input. But accessing user's location by an adversary invites issues of privacy breach. Instead of specific location coordinates, its surrounding area known as cloaking region is revealed in order to get the service. K anonymity technique of location privacy ensures that at least K-1 users should be included within a specific cloaked region. Researches have established that on combining K anonymity with the idea of including similar users together in a cloaked region provides stringent privacy (especially from background and heterogeneity attacks). This work quantifies the amount of privacy gain attained through, opting-for users with similar profiles instead of random users. The quantification is done by using KL divergence. Values of KL divergence of user profiles have been calculated for different cloaking regions containing similar and random users. Low KL divergence values depict privacy gains up to 33% for users with similar profiles.read more
References
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Journal ArticleDOI
Preventing Location-Based Identity Inference in Anonymous Spatial Queries
TL;DR: This work proposes transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without revealing the query source.
Journal ArticleDOI
An anonymous entropy-based location privacy protection scheme in mobile social networks
TL;DR: An anonymous entropy-based location privacy protection scheme in mobile social networks (MSN), which includes two algorithms K-DDCA in a densely populated region and K-SDCA in an sparsely populated region to tackle the problem of location privacy leakage.
Journal ArticleDOI
Quantifying privacy in terms of entropy for context aware services
TL;DR: The issue of privacy protection in context aware services, through the use of entropy as a means of measuring the capability of locating a user’s whereabouts and identifying personal selections, is addressed.
Journal ArticleDOI
SEACON: An Integrated Approach to the Analysis and Design of Secure Enterprise Architecture-Based Computer Networks
TL;DR: This work provides an integrated approach to use existing principles of information systems analysis and design with the unique requirements of distributed secure network systems to provide built-in mechanisms to capture security needs and use them seamlessly throughout the steps of analyzing and designing secure networks.
Journal ArticleDOI
Protecting User Privacy Better with Query l-Diversity
Fuyu Liu,Kien A. Hua +1 more
TL;DR: The authors propose two techniques: Expand Cloak and Hilbert Cloak to achieve query l-diversity, which better protect user privacy and compare the improved Interval Cloak technique through extensive simulation studies.