A Formal Model of Obfuscation and Negotiation for Location Privacy

By Matt Duckham, Lars Kulik. International Conference on Pervasive Computing, 2005

719 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/52c55ae1f68b91e358b71d6d9eb8eebf88116c83

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Citations in the corpus

Location Privacy Protection Through Obfuscation-Based Techniques (2007, methodology): “Duckham and Kulik [8] develop an obfuscation technique for protecting location privacy by artificially inserting into measurements some fake points with the same probability as the real user position.”
Protecting Locations with Differential Privacy under Temporal Correlations (2014, background): “ons proposed in the literature are based on location obfuscation which transforms the exact location of a user to an area (location generalization) or perturbed location (location perturbation) (e.g. [14, 20, 25]). Unfortunately, most spatial transformation techniques proposed so far rely on syntact”
Quantifying Location Privacy: The Case of Sporadic Location Exposure (2011, background): “Duckham and Kulik [4] propose a formal model for location obfuscation techniques such as adding inaccuracy, imprecision, and vagueness.”
Technical Privacy Metrics (2015, background): “…as the size of the crowd into which the target t can blend. priv ASS ≡ | AS t | Instead of users, anonymity sets can also be applied to locations [Duckham and Kulik 2005], location pairs (e.g., home/work) [Golle and Partridge 2009], or radio frequency identiﬁcation (RFID) devices…”
A survey of computational location privacy (2009, background): “In their excellent survey [15], Duckham and Kulik list four general methods for location privacy: • Regulatory strategies – normally government rules on the use of personal information • Privacy policies – trust-based agreements between individuals and whomever is receiving their location data • Ano”
Optimal Geo-Indistinguishable Mechanisms for Location Privacy (2014, methodology): “A common technique for this purpose is cloaking [23,24,25,21], which consists in blurring the user’s location by reporting a region to the service provider.”
SpaceTwist: Managing the Trade-Offs Among Location Privacy, Query Performance, and Query Accuracy in Mobile Services (2008, background): “In this setting, a negotiation protocol for trading between privacy and result accuracy has also been proposed [9], [16].”
Broadening the Scope of Differential Privacy Using Metrics (2013, background): “Others works [22,23,24] rely on the well-known concept of k -anonymity.”
Computing with Spatial Trajectories (2011, background): “2 To support LBS applications, the database community has made a tremendous research effort on the development of mobile object databases (MODs) in the past decade [33, 14, 13, 28].”
Geo-indistinguishability: differential privacy for location-based systems (2012, methodology): “Another method for achieving k-anonymity is through cloaking [6, 13, 38].”
On the Anonymity of Home/Work Location Pairs (2009, background): “Intentional degradation of location information quality, or obfuscation, is a wellknown technique for preserving the anonymity, or pseudonymity, of location traces [2, 3], but the question of how much obfuscation is required to preserve anonymity is often sidestepped.”
Location privacy in pervasive computing (2008, background): “This class includes all the solutions based on the notion of obfuscation [4] [5] [15] [41], which is the process of degrading the accuracy of the location information to provide privacy protection.”
The new Casper: query processing for location services without compromising privacy (2006, background): “Motivated by the privacy threats of location-detection devices [1, 6, 8, 44], several research eﬀorts are dedicated to protect the user location privacy (e.g., false dummies [27], landmark objects [21], and location perturbation [13, 16, 17]).”