Optimal Geo-Indistinguishable Mechanisms for Location Privacy
Not on the map. Why?
Abstract (arXiv)
We consider the geo-indistinguishability approach to location privacy, and the trade-off with respect to utility. We show that, given a desired degree of geo-indistinguishability, it is possible to construct a mechanism that minimizes the service quality loss, using linear programming techniques. In addition we show that, under certain conditions, such mechanism also provides optimal privacy in the sense of Shokri et al. Furthermore, we propose a method to reduce the number of constraints of the linear program from cubic to quadratic, maintaining the privacy guarantees and without affecting significantly the utility of the generated mechanism. This reduces considerably the time required to solve the linear program, thus enlarging significantly the location sets for which the optimal mechanisms can be computed.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2014.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 5; 2023: 1; 2024: 1; 2025: 0; 2026: 2.
9 papers.
- 2022Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Designcited by 6
- 2022Group privacy for personalized federated learningcited by 5
- 2022Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directionscited by 4
- 2022Privacy-Utility Trades in Crowdsourced Signal Map Obfuscationcited by 2
- 2023Where have you been? A Study of Privacy Risk for Point-of-Interest Recommendationcited by 2
- 2022An Easy-to-use and Robust Approach for the Differentially Private De-Identification of Clinical Textual Documentscited by 1
- 2024Enhancing Scalability of Metric Differential Privacy via Secret Dataset Partitioning and Benders Decompositioncited by 1
- 2026Interpolation-Based Optimization for Enforcing lp-Norm Metric Differential Privacy in Continuous and Fine-Grained Domainscited by 0
- 2026Metric-Normalized Posterior Leakage (mPL): Attacker-Aligned Privacy for Joint Consumptioncited by 0
- 2026Metric-Normalized Posterior Leakage (mPL): Attacker-Aligned Privacy for Joint Consumption1 May 2026
- 2026Interpolation-Based Optimization for Enforcing lp-Norm Metric Differential Privacy in Continuous and Fine-Grained Domains15 Jan 2026
- 2024Enhancing Scalability of Metric Differential Privacy via Secret Dataset Partitioning and Benders Decomposition7 May 2024
- 2023Where have you been? A Study of Privacy Risk for Point-of-Interest Recommendation28 Oct 2023
- 2022Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design8 Nov 2022
- 2022An Easy-to-use and Robust Approach for the Differentially Private De-Identification of Clinical Textual Documents2 Nov 2022
- 2022Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions8 Sep 2022
- 2022Group privacy for personalized federated learning7 Jun 2022
- 2022Privacy-Utility Trades in Crowdsourced Signal Map Obfuscation13 Jan 2022