Uncertainty in Artificial Intelligence
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SPPM: Sparse Privacy Preserving Mappings
Salman Salamatian, Nadia Fawaz, Branislav Kveton, Nina Taft
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.
Pages: 712-721
PS Link:
PDF Link: /papers/14/p712-salamatian.pdf
AUTHOR = "Salman Salamatian and Nadia Fawaz and Branislav Kveton and Nina Taft",
TITLE = "SPPM: Sparse Privacy Preserving Mappings",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "712--721"

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