SPPM: Sparse Privacy Preserving Mappings

Salman Salamatian, Nadia Fawaz Technicolor, Branislav Kveton Technicolor Labs, Nina Taft Technicolor
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:618-627, 2014.

Abstract

We study the problem of a user who has both public and private data, and wants to re- lease the public data, e.g. to a recommenda- tion service, yet simultaneously wants to pro- tect his private data from being inferred via big data analytics. This problem has previ- ously been formulated as a convex optimiza- tion problem with linear constraints where the objective is to minimize the mutual in- formation between the private and released data. This attractive formulation faces a challenge in practice because when the un- derlying alphabet of the user profile is large, there are too many potential ways to distort the original profile. We address this funda- mental scalability challenge. We propose to generate sparse privacy-preserving mappings by recasting the problem as a sequence of lin- ear programs and solving each of these in- crementally using an adaptation of Dantzig- Wolfe decomposition. We evaluate our ap- proach on several datasets and demonstrate that nearly optimal privacy-preserving map- pings can be learned quickly even at scale.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-salamatian14a, title = {{SPPM}: Sparse Privacy Preserving Mappings}, author = {Salamatian, Salman and Technicolor, Nadia Fawaz and Labs, Branislav Kveton Technicolor and Technicolor, Nina Taft}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {618--627}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/salamatian14a/salamatian14a.pdf}, url = {https://proceedings.mlr.press/r12/salamatian14a.html}, abstract = {We study the problem of a user who has both public and private data, and wants to re- lease the public data, e.g. to a recommenda- tion service, yet simultaneously wants to pro- tect his private data from being inferred via big data analytics. This problem has previ- ously been formulated as a convex optimiza- tion problem with linear constraints where the objective is to minimize the mutual in- formation between the private and released data. This attractive formulation faces a challenge in practice because when the un- derlying alphabet of the user profile is large, there are too many potential ways to distort the original profile. We address this funda- mental scalability challenge. We propose to generate sparse privacy-preserving mappings by recasting the problem as a sequence of lin- ear programs and solving each of these in- crementally using an adaptation of Dantzig- Wolfe decomposition. We evaluate our ap- proach on several datasets and demonstrate that nearly optimal privacy-preserving map- pings can be learned quickly even at scale.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T SPPM: Sparse Privacy Preserving Mappings %A Salman Salamatian %A Nadia Fawaz Technicolor %A Branislav Kveton Technicolor Labs %A Nina Taft Technicolor %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-salamatian14a %I PMLR %P 618--627 %U https://proceedings.mlr.press/r12/salamatian14a.html %V R12 %X We study the problem of a user who has both public and private data, and wants to re- lease the public data, e.g. to a recommenda- tion service, yet simultaneously wants to pro- tect his private data from being inferred via big data analytics. This problem has previ- ously been formulated as a convex optimiza- tion problem with linear constraints where the objective is to minimize the mutual in- formation between the private and released data. This attractive formulation faces a challenge in practice because when the un- derlying alphabet of the user profile is large, there are too many potential ways to distort the original profile. We address this funda- mental scalability challenge. We propose to generate sparse privacy-preserving mappings by recasting the problem as a sequence of lin- ear programs and solving each of these in- crementally using an adaptation of Dantzig- Wolfe decomposition. We evaluate our ap- proach on several datasets and demonstrate that nearly optimal privacy-preserving map- pings can be learned quickly even at scale. %Z Reissued by PMLR on 04 October 2026.
APA
Salamatian, S., Technicolor, N.F., Labs, B.K.T. & Technicolor, N.T.. (2014). SPPM: Sparse Privacy Preserving Mappings. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:618-627 Available from https://proceedings.mlr.press/r12/salamatian14a.html. Reissued by PMLR on 04 October 2026.

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