Journal ArticleDOI
A survey of computational location privacy
John Krumm
- Vol. 13, Iss: 6, pp 391-399
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TLDR
This is a literature survey of computational location privacy, meaning computation-based privacy mechanisms that treat location data as geometric information, which includes privacy-preserving algorithms like anonymity and obfuscation as well as privacy-breaking algorithms that exploit the geometric nature of the data.Abstract:
This is a literature survey of computational location privacy, meaning computation-based privacy mechanisms that treat location data as geometric information. This definition includes privacy-preserving algorithms like anonymity and obfuscation as well as privacy-breaking algorithms that exploit the geometric nature of the data. The survey omits non-computational techniques like manually inspecting geotagged photos, and it omits techniques like encryption or access control that treat location data as general symbols. The paper reviews studies of peoples' attitudes about location privacy, computational threats on leaked location data, and computational countermeasures for mitigating these threats.read more
Citations
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Mobile crowdsensing: current state and future challenges
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TL;DR: The need for a unified architecture for mobile crowdsensing is argued and the requirements it must satisfy are envisioned.
Proceedings ArticleDOI
Geo-indistinguishability: differential privacy for location-based systems
TL;DR: In this article, the authors 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.
Proceedings ArticleDOI
Quantifying Location Privacy
TL;DR: This paper provides a formal framework for the analysis of LPPMs, it captures the prior information that might be available to the attacker, and various attacks that he can perform, and clarifies the difference between three aspects of the adversary's inference attacks, namely their accuracy, certainty, and correctness.
Proceedings ArticleDOI
Differentially private aggregation of distributed time-series with transformation and encryption
Vibhor Rastogi,Suman Nath +1 more
TL;DR: The Distributed Laplace Perturbation Algorithm (DLPA) is the first distributed differentially private algorithm that can scale with a large number of users: DLPA outperforms the only other distributed solution for differential privacy proposed so far, by reducing the computational load per user from O(U) to O(1) where U is the number of Users.
References
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Proceedings ArticleDOI
The Cricket location-support system
TL;DR: The randomized algorithm used by beacons to transmit information, the use of concurrent radio and ultrasonic signals to infer distance, the listener inference algorithms to overcome multipath and interference, and practical beacon configuration and positioning techniques that improve accuracy are described.
Proceedings ArticleDOI
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Marco Gruteser,Dirk Grunwald +1 more
TL;DR: A middleware architecture and algorithms that can be used by a centralized location broker service that adjusts the resolution of location information along spatial or temporal dimensions to meet specified anonymity constraints based on the entities who may be using location services within a given area.
Journal ArticleDOI
Achieving k -anonymity privacy protection using generalization and suppression
TL;DR: This paper provides a formal presentation of combining generalization and suppression to achieve k-anonymity and shows that Datafly can over distort data and µ-Argus can additionally fail to provide adequate protection.
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