Location Privacy in Mobile Systems: A Personalized Anonymization Model

By Bugra Gedik, Ling Liu. IEEE International Conference on Distributed Computing Systems, 2005

810 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/01a29e319e2afa2d29cab62ef1f492a953e8ca70

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Citations in the corpus

Geo-indistinguishability: differential privacy for location-based systems (2012, background): “Many systems in this category [21, 19, 30] aim at protecting the user’s identity, requiring that the attacker cannot infer which user is executing the query, among a set of k different users.”
Achieving k-anonymity in privacy-aware location-based services (2014, background): “Most of the existing approaches [6], [7], [8], [4], [9] assume that the adversary has no side information [10], [11], such as user’s query probability related to location and time, and information related to the semantics of the query such as the gender and social status of the user, and then dummy ”
Anonysense: privacy-aware people-centric sensing (2008, background): “There is a wealth of research focused on this problem, and our architecture can be extended to include techniques such as k-anonymity and spatio-temporal cloaking [35, 15, 14, 19, 17] to reduce the leakage of private data through reports.”
Optimal Geo-Indistinguishable Mechanisms for Location Privacy (2014, methodology): “Several approaches are based in the notion of k-anonymity [25, 26, 27], requiring that the attacker cannot identify a user from at least other k−1 different users.”
Virtual trip lines for distributed privacy-preserving traffic monitoring (2008, background): “For location services, the k-anonymity concept has lead to the development of centralized architectures that temporally and spatially cloak location-based queries [30, 48, 27].”
Location privacy in pervasive computing (2008, background): “In particular, this class includes all solutions based on the notion of anonymity [9] [10] [21] [24], which is aimed at making an individual (i.e., her identity or personal information) not identifiable.”
Preserving privacy in gps traces via uncertainty-aware path cloaking (2007, methodology): “The results were obtained with the CliqueCloak algorithm [22], which to our knowledge achieves the best accuracy.”
Privacy and accountability for location-based aggregate statistics (2011, background): “Some previous work ensures that clients upload data free of identi-ﬁers (they upload approximate time, location, and data sample), but even when all data is anonymized, a considerable amount of private location information can still leak due to side information.”
Protecting location privacy: optimal strategy against localization attacks (2012, background): “The most popular approach to obtaining location privacy is to send a space-or time-obfuscated version of the users’ actual locations to the service provider [10, 12, 14, 16].”
Application of Local Differential Privacy to Collection of Indoor Positioning Data (2018, background): “That is, a user transmits her/his indoor location information to the centralized trusted server, which in turn perturbs the user’s precise indoor location information to satisfy the privacy requirements and transmits it to the big data collection server [10]–[12], [24], [36].”
Private spatial data aggregation in the local setting (2016, background): “Gedik and Liu [21] present a personalized k -anonymity model for sharing location information.”
PRIVE: anonymous location-based queries in distributed mobile systems (2007, methodology): “[10, 11], the location of the user is concealed by constructing an Anonymizing Spatial Region ( K -ASR) which encloses the locations of the query source and K− 1 additional users.”
Dissent in Numbers: Making Strong Anonymity Scale (2012, neutral): “As everyday computing shifts to mobile devices, an ongoing challenge is to offer users the same privacy protections on phones as they would have on desktop computers [8,33,34].”
Quantifying and protecting location privacy (2015, positive): “A large body of research has focused on developing location-privacy protection mechanisms (LPPMs) that allow users to make use of LBSs and limit the amount of disclosed sensitive information [1, 5, 9, 11, 13, 14, 18].”
Private queries in location based services: anonymizers are not necessary (2008, methodology): “To enforce this principle, a trusted third-party anonymizer is employed [11, 14, 17, 20] (see Figure 1).”
Secure kNN computation on encrypted databases (2009, background): “In particular, they choose a common encryption scheme.”