Name
Abstract | ||
|---|---|---|
In the context of a single report of location information, existing researches define location privacy by adversary's uncertainty, inaccuracy, or incorrectness of the estimation, or by geo-indistinguishability which is a generalization of differential privacy. Each of these existing notions has problems in some specific scenarios. In this paper we illustrate the limitations of existing notions by constructing such scenarios, and introduce a formal definition on location privacy by quantifying the distance between the prior and posterior distribution over the possible locations. Further more, we show how to construct a near-optimal obfuscation mechanism by solving an optimization problem. We compare our proposed mechanism with the Laplace noise based geo-indistinguishable mechanism, and Shokri's optimal obfuscation mechanism, using both our proposed privacy metric and the traditional metric based on the estimated distance errors. The results show that our proposed metric better describes location privacy and our proposed mechanism makes a better tradeoff between privacy and utility. (C) 2017 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.future.2017.05.045 | FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE |
Keywords | Field | DocType |
Location privacy,Differential privacy,Obfuscation | Data mining,Differential privacy,Computer science,Posterior probability,Formal description,Adversary,Obfuscation,Optimization problem,Privacy software | Journal |
Volume | ISSN | Citations |
86 | 0167-739X | 2 |
PageRank | References | Authors |
0.40 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kai Dong | 1 | 14 | 4.63 |
| Taolin Guo | 2 | 4 | 1.10 |
| Haibo Ye | 3 | 95 | 8.92 |
| Xuansong Li | 4 | 72 | 9.93 |
| Zhen Ling | 5 | 209 | 25.15 |