Preventing Location-Based Identity Inference in Anonymous Spatial Queries

By Panos Kalnis, Gabriel Ghinita, Kyriakos Mouratidis, Dimitris Papadias. IEEE Transactions on Knowledge and Data Engineering, 2007

754 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/6dfb1e19c280fef47453acf5485f8c157ded1609

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Citations in the corpus

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].”
EnCore: private, context-based communication for mobile social apps (2014, background): “The first class proposes to send fake or perturbed location data, or send location data at coarser granularity [38, 53, 32, 35, 41].”
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].”
Private queries in location based services: anonymizers are not necessary (2008, methodology): “Hilbert Cloak guarantees privacy for any distribution of user locations.”
SpaceTwist: Managing the Trade-Offs Among Location Privacy, Query Performance, and Query Accuracy in Mobile Services (2008, background): “Figure 1a exemplifies the case where Q′ is a rectangle [3], [4].”
PRIVE: anonymous location-based queries in distributed mobile systems (2007, methodology): “[13, 17] extend this”
Virtual trip lines for distributed privacy-preserving traffic monitoring (2008, background): “The k-anonymity concept [53, 56] provides a guaranteed level of anonymity for a database, although some recent studies [46, 40] have identified weaknesses.”
Computing with Spatial Trajectories (2011, methodology): “Motivated by these observations, many researchers have focused on addressing the problem of uncertainty management in MOD settings [5, 21, 31, 30, 67, 66, 83, 85, 86, 91, 94, 108, 113, 115, 114, 143, 149, 145, 159, 160], also considering the impact of the restriction of the motions to road networks ”
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].”
The new Casper: query processing for location services without compromising privacy (2006, methodology): “The difference between these two works is that the work [Hu and Lee 2006] considers rectilinear cloaked areas while the other one considers circular cloaked areas [Kalnis et al. 2007].”