# Location Trace Privacy Under Conditional Priors

arXiv: https://arxiv.org/abs/2102.11955 · HTML: https://mlsanity.com/paper/2102.11955/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 23 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [Renyi Differential Privacy](https://mlsanity.com/paper/1702.07476/) (2017)
- [Deep Neural Networks as Gaussian Processes](https://mlsanity.com/paper/1711.00165/) (2017)
- [Protecting Locations with Differential Privacy under Temporal Correlations](https://mlsanity.com/paper/1410.5919/) (2014)
- [Pufferfish Privacy Mechanisms for Correlated Data](https://mlsanity.com/paper/1603.03977/) (2016)
- [Quantifying Differential Privacy under Temporal Correlations](https://mlsanity.com/paper/1610.07543/) (2016)
- [A Predictive Differentially-Private Mechanism for Mobility Traces](https://mlsanity.com/paper/1311.4008/) (2013)
- [Composition Properties of Inferential Privacy for Time-Series Data](https://mlsanity.com/paper/1707.02702/) (2017)
- [PriSTE: From Location Privacy to Spatiotemporal Event Privacy](https://mlsanity.com/paper/1810.09152/) (2018)

## Built on by (most cited first, 1 of 1)

- [Privacy Amplification by Subsampling in Time Domain](https://mlsanity.com/paper/2201.04762/) (2022)

## Abstract (arXiv)

Providing meaningful privacy to users of location based services is particularly challenging when multiple locations are revealed in a short period of time. This is primarily due to the tremendous degree of dependence that can be anticipated between points. We propose a R'enyi divergence based privacy framework for bounding expected privacy loss for conditionally dependent data. Additionally, we demonstrate an algorithm for achieving this privacy under Gaussian process conditional priors. This framework both exemplifies why conditionally dependent data is so challenging to protect and offers a strategy for preserving privacy to within a fixed radius for sensitive locations in a user's trace.