Privacy via the Johnson-Lindenstrauss Transform
Journal of Privacy and Confidentiality, pp. 22013.
Abstract:
Suppose that party A collects private information about its users, where each user\u0027s data is represented as a bit vector. Suppose that party B has a proprietary data mining algorithm that requires estimating the distance between users, such as clustering or nearest neighbors. We ask if it is possible for party A to publish some infor...More
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