The new Casper: query processing for location services without compromising privacy

By Mohamed F. Mokbel, Chi-Yin Chow, Walid G. Aref. Very Large Data Bases Conference, 2006

1,309 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/008587de3cc3a1296e693164ce0e158bb2e7f117

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Optimal Geo-Indistinguishable Mechanisms for Location Privacy (2014, background): “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.”
Semantic trajectories modeling and analysis (2013, unknown): “These guidelines have been embodied in the privacy-preserving framework called Probe (PRivacy-aware OBfuscation Environment) [Damiani et al. 2010].”
Blind Evaluation of Nearest Neighbor Queries Using Space Transformation to Preserve Location Privacy (2007, background): “Several techniques based on cloaking and K-anonymity have been proposed in the literature to reduce the resolution of the user’s location information [1, 2, 4–6, 13, 14].”
Privacy Preservation in the Publication of Trajectories (2008, background): “Spatial cloaking was first proposed in [8] and based on this idea, a system that supports different queries and various anonymization requirements was developed by Mokbel et al. in [15].”
Location Privacy Protection Through Obfuscation-Based Techniques (2007, result): “Same assumption can be found in other works on this topic [12].”
Private queries in location based services: anonymizers are not necessary (2008, methodology): “Moreover, only K users are included in a cloaking region CR, and K |U ′|, otherwise CR grows large and the query cost becomes prohibitive (typically K is in the order of 10(2) [17, 20]).”
Privacy in mobile technology for personal healthcare (2012, methodology): “Some consider anonymous use of location-based services, for example, using techniques for blurring location and time [Mokbel et al. 2006; Gruteser and Grunwald 2003]; these methods have been extended to support personalized k-anonymity [Gedik and Liu 2008].”
Preventing Location-Based Identity Inference in Anonymous Spatial Queries (2007, methodology): “Finally, Ghinita et al. [14] employ multidimensional to one-dimensional (1D) transformations to solve efficiently the K -anonymity and ‘ -diversity problems.”
A peer-to-peer spatial cloaking algorithm for anonymous location-based service (2006, methodology): “To tackle this major privacy concern, three centralized privacy-preserving frameworks are proposed for LBS [13, 14, 31 ], in which a trusted third party is used as a middleware to blur user locations into spatial regions to achieve k-anonymity, i.e., a user is indistinguishable among other k − 1 us”
Preserving privacy in gps traces via uncertainty-aware path cloaking (2007, background): “Specifically, the k-anonymity concept [38, 42] has been adapted for location-based services [24, 37, 22].”
A survey of computational location privacy (2009, background): “[47] developed a new query processor for location databases that lets a user specify a guaranteed k and the minimum area within which he or she wants to hide.”
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].”
SpaceTwist: Managing the Trade-Offs Among Location Privacy, Query Performance, and Query Accuracy in Mobile Services (2008, unknown): “Spatial cloaking techniques [3]–[9] enlarge an exact user location q into a cloaked region Q′ so that it is impossible to reconstruct q from the region Q′ at the server.”
Secure kNN computation on encrypted databases (2009, background): “In particular, they choose a common encryption scheme.”
A Framework for Protecting Worker Location Privacy in Spatial Crowdsourcing (2014, background): “However, the SC-server may not be trusted, and disclosing individual locations has serious privacy implications [10, 22, 8, 4].”