Location Trace Privacy Under Conditional Priors

Casey Meehan, Kamalika Chaudhuri
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:2881-2889, 2021.

Abstract

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 Rényi 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v130-meehan21a, title = { Location Trace Privacy Under Conditional Priors }, author = {Meehan, Casey and Chaudhuri, Kamalika}, booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics}, pages = {2881--2889}, year = {2021}, editor = {Banerjee, Arindam and Fukumizu, Kenji}, volume = {130}, series = {Proceedings of Machine Learning Research}, month = {13--15 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v130/meehan21a/meehan21a.pdf}, url = {https://proceedings.mlr.press/v130/meehan21a.html}, abstract = { 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 Rényi 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. } }
Endnote
%0 Conference Paper %T Location Trace Privacy Under Conditional Priors %A Casey Meehan %A Kamalika Chaudhuri %B Proceedings of The 24th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2021 %E Arindam Banerjee %E Kenji Fukumizu %F pmlr-v130-meehan21a %I PMLR %P 2881--2889 %U https://proceedings.mlr.press/v130/meehan21a.html %V 130 %X 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 Rényi 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.
APA
Meehan, C. & Chaudhuri, K.. (2021). Location Trace Privacy Under Conditional Priors . Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 130:2881-2889 Available from https://proceedings.mlr.press/v130/meehan21a.html.

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