NDLI: Privacy tradeoffs in predictive analytics

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Privacy tradeoffs in predictive analytics

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Privacy tradeoffs in predictive analytics

Content Provider	ACM Digital Library
Author	Bhagat, Smriti Montanari, Andrea Fawaz, Nadia Weinsberg, Udi Ioannidis, Stratis Taft, Nina
Abstract	Online services routinely mine user data to predict user preferences, make recommendations, and place targeted ads. Recent research has demonstrated that several private user attributes (such as political affiliation, sexual orientation, and gender) can be inferred from such data. Can a privacy-conscious user benefit from personalization while simultaneously protecting her private attributes? We study this question in the context of a rating prediction service based on matrix factorization. We construct a protocol of interactions between the service and users that has remarkable optimality properties: it is privacy-preserving, in that no inference algorithm can succeed in inferring a user's private attribute with a probability better than random guessing; it has maximal accuracy, in that no other privacy-preserving protocol improves rating prediction; and, finally, it involves a minimal disclosure, as the prediction accuracy strictly decreases when the service reveals less information. We extensively evaluate our protocol using several rating datasets, demonstrating that it successfully blocks the inference of gender, age and political affiliation, while incurring less than 5% decrease in the accuracy of rating prediction.
Starting Page	57
Ending Page	69
Page Count	13
File Format	PDF
ISBN	9781450327893
DOI	10.1145/2591971.2592011
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-06-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Matrix factorization Privacy-preserving protocols
Content Type	Text
Resource Type	Article

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