Differentially Private One Permutation Hashing
Primary Area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
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Keywords: differential privacy, hashing
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Abstract: Minwise hashing (MinHash) is a standard algorithm for large-scale search and learning with the binary Jaccard similarity. One permutation hashing (OPH) is an effective and efficient alternative of MinHash which splits the data into K bins and generates hash values within each bin. In this paper, we combine differential privacy (DP) with OPH, and propose DP-OPH framework with three variants: DP-OPH-fix, DP-OPH-re and DP-OPH-rand, depending on the densification strategy to deal with empty bins in OPH. A detailed roadmap to the algorithm design is presented along with the privacy analysis. Analytical comparison of our DP-OPH methods with the DP minwise hashing (DP-MH) alternative is provided to justify the advantage of DP-OPH. Experiments on similarity search confirms the merits of our proposed DP-OPH algorithms, and provide guidance on the choice of proper variant in different practical scenarios.
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Submission Number: 4560
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