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Journal ArticleDOI

Entropy-Based Quantification of Privacy Attained Through User Profile Similarity

TL;DR: 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.
References
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Journal ArticleDOI
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.
Abstract: The increasing trend of embedding positioning capabilities (for example, GPS) in mobile devices facilitates the widespread use of location-based services. For such applications to succeed, privacy and confidentiality are essential. Existing privacy-enhancing techniques rely on encryption to safeguard communication channels, and on pseudonyms to protect user identities. Nevertheless, the query contents may disclose the physical location of the user. In this paper, we present a framework for preventing location-based identity inference of users who issue spatial queries to location-based services. We propose transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without revealing the query source. Our methods optimize the entire process of anonymizing the requests and processing the transformed spatial queries. Extensive experimental studies suggest that the proposed techniques are applicable to real-life scenarios with numerous mobile users.

686 citations

Journal ArticleDOI
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.
Abstract: The popularization of mobile communication devices and location technology has spurred the increasing demand for location-based services (LBSs). While enjoying the convenience provided by LBS, users may be confronted with the risk of privacy leakage. It is very crucial to devise a secure scheme to protect the location privacy of users. In this paper, we propose 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 a sparsely populated region to tackle the problem of location privacy leakage. The K-DDCA algorithm employs anonymous entropy method to select user groups and construct anonymous regions which can guarantee the area of the anonymous region formed be moderate and the diversity of the request content. The K-SDCA algorithm generates a set of similar dummy locations which can resist the attack of adversaries with background information. Particularly, we present the anonymous entropy method based on the location distance and request contents. The effectiveness of our scheme is validated through extensive simulations, which show that our scheme can achieve enhanced privacy preservation and better efficiency.

34 citations

Journal ArticleDOI
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.
Abstract: In this paper, we address 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. We present a framework for calculating levels of abstraction in location and personal preferences reporting in queries to a context aware services server. Finally, we propose a methodology for determining the levels of abstraction in location and preferences that should be applied in user data reporting during service provision, according to her personal privacy settings.

13 citations

Journal ArticleDOI
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.
Abstract: The extent methods largely ignore the importance of integrating security requirements with business requirements and providing built-in steps for dealing with these requirements seamlessly. To address this problem, a new approach to secure network analysis and design is presented. The proposed method, called the SEACON method, provides an integrated approach to use existing principles of information systems analysis and design with the unique requirements of distributed secure network systems. We introduce several concepts including security adequacy level, process-location-security matrix, data-location- security matrix, and secure location model to provide built-in mechanisms to capture security needs and use them seamlessly throughout the steps of analyzing and designing secure networks. This method is illustrated and compared to other secure network design methods. The SEACON method is found to be a useful and effective method.

7 citations

Journal ArticleDOI
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.
Abstract: This paper examines major privacy concerns in location-based services. Most user privacy techniques are based on cloaking, which achieves location k-anonymity. The key is to reduce location resolution by ensuring that each cloaking area reported to a service provider contains at least k mobile users. However, maintaining location k-anonymity alone is inadequate when the majority of the k mobile users are interested in the same query subject. In this paper, the authors address this problem by defining a novel concept called query l-diversity, which requires diversified queries submitted from the k users. The authors propose two techniques: Expand Cloak and Hilbert Cloak to achieve query l-diversity. To show the effectiveness of the proposed techniques, they compare the improved Interval Cloak technique through extensive simulation studies. The results show that these techniques better protect user privacy.

7 citations