Geo-indistinguishability: differential privacy for location-based systems

By Miguel E. Andrés, Nicolás Emilio Bordenabe, Konstantinos Chatzikokolakis, Catuscia Palamidessi. 2012

1,314 citations recorded by the source corpus.

The growing popularity of location-based systems, allowing unknown/untrusted servers to easily collect huge amounts of information regarding users' location, has recently started raising serious privacy concerns. In this paper we introduce geoind, a formal notion of privacy for location-based systems that protects the user's exact location, while allowing approximate information -- typically needed to obtain a certain desired service -- to be released. This privacy definition formalizes the intuitive notion of protecting the user's location within a radius $r$ with a level of privacy that depends on r, and corresponds to a generalized version of the well-known concept of differential privacy. Furthermore, we present a mechanism for achieving geoind by adding controlled random noise to the user's location. We describe how to use our mechanism to enhance LBS applications with geo-indistinguishability guarantees without compromising the quality of the application results. Finally, we compare state-of-the-art mechanisms from the literature with ours. It turns out that, among all mechanisms independent of the prior, our mechanism offers the best privacy guarantees.

Subfields: Artificial Intelligence, Sociology and Political Science

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Citations in the corpus

Advanced Probabilistic Couplings for Differential Privacy (2016, neutral): “This and other possible generalizations of sparse vector could bring interesting results in domains like geo-indistinguishability [1].”
Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples (2016, methodology): “We obtained a subset of their location dataset which had a total of 196, 591 users and 6, 442, 890 check-ins of these users over the period from February 2009 to October 2010.”
What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacy (2017, result): “dence. Moreover, our results motivate future work towards the design of new differential privacy techniques that take into account temporal as well as spatial correlations, such as those discussed in [3,6] which may provide a promising direction. Overall, we highlight the need for novel defense mech”
Local Differential Private Data Aggregation for Discrete Distribution Estimation (2019, methodology): “Definition 1 ( -LDP [17], [18], [23]).”
Study : Privacy Preserving Release of Spatio-temporal Density in Paris (2014, background): “Another recent work [2] formalizes location privacy as protecting the users’ location within a radius r with a level of privacy that depends on r.”
REACT (2020, methodology): “Currently, we have adopted and evaluated the geo-indistinguishability (GeoInd) privacy deﬁnition [4, 42, 39, 17] to enable users to protect their locations.”
Toward Distribution Estimation under Local Differential Privacy with Small Samples (2018, background): “, provides POI (point of interest) information nearby the noisy location [4]) to the users.”
Protecting Locations with Differential Privacy under Temporal Correlations (2014, methodology): “On the other hand, If we view the space as a map with longitude and latitude, then a 2 × 1 vector can be used to represent a user’s location x with two components x [1] and x [2].”
DP-WHERE: Differentially private modeling of human mobility (2013, background): “[2] introduce the notion of geo-indistinguishability in location-based systems, which protects the exact location of a user while allowing release of information needed to gain access to a service.”
Privacy Preserving Image-Based Localization (2019, methodology): “Differential privacy [20] and k-anonymity [56] have been applied to the problem of location privacy [3, 6, 26, 72].”
Practical and Robust Privacy Amplification with Multi-Party Differential Privacy (2019, background): “Both usages are similar to the geo-indistinguishability notion in the centralized setting [6].”
Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics (2017, background): “These have led to the design of application speciﬁc privacy notions (e.g., [16, 39]).”
A Survey on Differential Privacy for Unstructured Data Content (2022, methodology): “Chebyshev distance is taken in obfuscating the reading data of smart meters for the privacy of TV channels being watched [3], while Manhattan distance is adopted in randomizing individuals’ registration date on Facebook against adversaries’ identification of individuals [3].”
Optimal Geo-Indistinguishable Mechanisms for Location Privacy (2014, methodology): “perturbing somehow the information reported to the LBS provider, in order to make it difficult for the adversary to infer the user’s true location [4,5,6,7,8,9].”