Differential Privacy with Random Projections and Sign Random Projections
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In this paper, we develop a series of differential privacy (DP) algorithms from a family of random projections (RP), for general applications in machine learning, data mining, and information retrieval. Among the presented algorithms, iDP-SignRP is remarkably effective under the setting of “individual differential privacy” (iDP), based on sign random projections (SignRP). Also, DP-SignOPORP considerably improves existing algorithms in the literature under the standard DP setting, using “one permutation + one random projection” (OPORP), where OPORP is a variant of the celebrated count-sketch method with fixed-length binning and normalization. Without taking signs, among the DP-RP family, DP-OPORP achieves the best performance. The concept of iDP (individual differential privacy) is defined only on a particular dataset of interest. While iDP is not strictly DP, iDP might be useful in certain applications, such as releasing a dataset (including sharing embeddings across companies or countries). In our study, we find that iDP-SignRP is remarkably effective for search and machine learning applications, in that the utilities are exceptionally good even at a very small privacy parameter ϵ (e.g., ϵ<0.5).
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