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The Normal Distributions Indistinguishability Spectrum and its Application to Privacy-Preserving Machine Learning
We apply the NDIS theorem to derive DP mechanisms for queries with normally-distributed outputs -- i. e., Gaussian Random Projections (RP) -- and for more general queries -- i. e., Ordinary Least Squares (OLS).
PD-ML-Lite: Private Distributed Machine Learning from Lighweight Cryptography
We apply our methodology to two major ML algorithms, namely non-negative matrix factorization (NMF) and singular value decomposition (SVD).
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