Protecting Privacy Against Location-Based Personal Identification

By Claudio Bettini, X. Wang, Sushil Jajodia. Secure Data Management, 2005

390 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/bd5f79f0ec499db1abeeef7f93b92fb4bc95684a

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Citations in the corpus

Unraveling an old cloak: k-anonymity for location privacy (2010, background): “A large body of research has focused on increasing the efficiency of kanonymity schemes and reducing their cost of query obfuscation [4, 12, 19, 20], extending the obfuscation method to protect traces [2] (i.”
Privacy Preservation in the Publication of Trajectories (2008, background): “In [3], the concept of historical k-anonymity is defined.”
A survey of computational location privacy (2009, background): “[7] declare that a sequence of recorded locations for a person constitutes a quasi-identifier, i.”
It's the way you check-in: identifying users in location-based social networks (2014, background): “In particular, the problem of protecting privacy in LBSNs has also been the subject of several studies, such as [4, 17, 19, 33, 7, 22, 28, 29].”
Location Privacy Protection Through Obfuscation-Based Techniques (2007, methodology): “[7] propose a framework in charge of evaluating the risk of sensitive locationbased information dissemination, and a technique aimed at supporting k-anonymity [14].”
Virtual trip lines for distributed privacy-preserving traffic monitoring (2008, background): “There are many best effort approaches [14, 54, 45, 35, 15] that degrade information in a controlled way before releasing it.”
Technical Privacy Metrics (2015, background): “Thus, historical kanonymity defines (time, location) pairs as quasi-identifiers and requires that the adversary cannot link a request to an individual user, but only to k or more users [17].”
Preserving privacy in gps traces via uncertainty-aware path cloaking (2007, background): “Thus, several best effort location data protection algorithms have been suggested [7, 39, 36, 27, 8], which have in common that they create areas of confusion where the traces from several users converge.”
Location privacy in pervasive computing (2008, background): “4 based on the personal identification of a user for their provision [9, 10, 21, 24].”
Preventing Location-Based Identity Inference in Anonymous Spatial Queries (2007, background): “For a broad discussion on the risks of revealing sensitive information in LBSs, see [7].”
Protecting Locations with Differential Privacy under Temporal Correlations (2014, background): “However, anonymity or ad hoc uncertainty based techniques do not always provide suﬃcient privacy protection [8, 12, 23, 34, 52].”