Preserving Privacy of Continuous High-dimensional Data with Minimax Filters

Jihun Hamm
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, PMLR 38:324-332, 2015.

Abstract

Preserving privacy of high-dimensional and continuous data such as images or biometric data is a challenging problem. This paper formulates this problem as a learning game between three parties: 1) data contributors using a filter to sanitize data samples, 2) a cooperative data aggregator learning a target task using the filtered samples, and 3) an adversary learning to identify contributors using the same filtered samples. Minimax filters that achieve the optimal privacy-utility trade-off from broad families of filters and loss/classifiers are defined, and algorithms for learning the filers in batch or distributed settings are presented. Experiments with several real-world tasks including facial expression recognition, speech emotion recognition, and activity recognition from motion, show that the minimax filter can simultaneously achieve similar or better target task accuracy and lower privacy risk, often significantly lower than previous methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v38-hamm15, title = {{Preserving Privacy of Continuous High-dimensional Data with Minimax Filters}}, author = {Hamm, Jihun}, booktitle = {Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics}, pages = {324--332}, year = {2015}, editor = {Lebanon, Guy and Vishwanathan, S. V. N.}, volume = {38}, series = {Proceedings of Machine Learning Research}, address = {San Diego, California, USA}, month = {09--12 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v38/hamm15.pdf}, url = {https://proceedings.mlr.press/v38/hamm15.html}, abstract = {Preserving privacy of high-dimensional and continuous data such as images or biometric data is a challenging problem. This paper formulates this problem as a learning game between three parties: 1) data contributors using a filter to sanitize data samples, 2) a cooperative data aggregator learning a target task using the filtered samples, and 3) an adversary learning to identify contributors using the same filtered samples. Minimax filters that achieve the optimal privacy-utility trade-off from broad families of filters and loss/classifiers are defined, and algorithms for learning the filers in batch or distributed settings are presented. Experiments with several real-world tasks including facial expression recognition, speech emotion recognition, and activity recognition from motion, show that the minimax filter can simultaneously achieve similar or better target task accuracy and lower privacy risk, often significantly lower than previous methods.} }
Endnote
%0 Conference Paper %T Preserving Privacy of Continuous High-dimensional Data with Minimax Filters %A Jihun Hamm %B Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2015 %E Guy Lebanon %E S. V. N. Vishwanathan %F pmlr-v38-hamm15 %I PMLR %P 324--332 %U https://proceedings.mlr.press/v38/hamm15.html %V 38 %X Preserving privacy of high-dimensional and continuous data such as images or biometric data is a challenging problem. This paper formulates this problem as a learning game between three parties: 1) data contributors using a filter to sanitize data samples, 2) a cooperative data aggregator learning a target task using the filtered samples, and 3) an adversary learning to identify contributors using the same filtered samples. Minimax filters that achieve the optimal privacy-utility trade-off from broad families of filters and loss/classifiers are defined, and algorithms for learning the filers in batch or distributed settings are presented. Experiments with several real-world tasks including facial expression recognition, speech emotion recognition, and activity recognition from motion, show that the minimax filter can simultaneously achieve similar or better target task accuracy and lower privacy risk, often significantly lower than previous methods.
RIS
TY - CPAPER TI - Preserving Privacy of Continuous High-dimensional Data with Minimax Filters AU - Jihun Hamm BT - Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics DA - 2015/02/21 ED - Guy Lebanon ED - S. V. N. Vishwanathan ID - pmlr-v38-hamm15 PB - PMLR DP - Proceedings of Machine Learning Research VL - 38 SP - 324 EP - 332 L1 - http://proceedings.mlr.press/v38/hamm15.pdf UR - https://proceedings.mlr.press/v38/hamm15.html AB - Preserving privacy of high-dimensional and continuous data such as images or biometric data is a challenging problem. This paper formulates this problem as a learning game between three parties: 1) data contributors using a filter to sanitize data samples, 2) a cooperative data aggregator learning a target task using the filtered samples, and 3) an adversary learning to identify contributors using the same filtered samples. Minimax filters that achieve the optimal privacy-utility trade-off from broad families of filters and loss/classifiers are defined, and algorithms for learning the filers in batch or distributed settings are presented. Experiments with several real-world tasks including facial expression recognition, speech emotion recognition, and activity recognition from motion, show that the minimax filter can simultaneously achieve similar or better target task accuracy and lower privacy risk, often significantly lower than previous methods. ER -
APA
Hamm, J.. (2015). Preserving Privacy of Continuous High-dimensional Data with Minimax Filters. Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 38:324-332 Available from https://proceedings.mlr.press/v38/hamm15.html.

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