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PrivateLR

Differentially Private Regularized Logistic Regression

Implements two differentially private algorithms for estimating L2-regularized logistic regression coefficients. A randomized algorithm F is epsilon-differentially private (C. Dwork, Differential Privacy, ICALP 2006 <DOI:10.1007/11681878_14>), if |log(P(F(D) in S)) - log(P(F(D') in S))| <= epsilon for any pair D, D' of datasets that differ in exactly one record, any measurable set S, and the randomness is taken over the choices F makes.

Versions across snapshots

VersionRepositoryFileSize
1.2-22 rolling linux/jammy R-4.5 PrivateLR_1.2-22.tar.gz 47.0 KiB
1.2-22 rolling linux/noble R-4.5 PrivateLR_1.2-22.tar.gz 46.9 KiB
1.2-22 rolling source/ R- PrivateLR_1.2-22.tar.gz 9.5 KiB
1.2-22 latest linux/jammy R-4.5 PrivateLR_1.2-22.tar.gz 47.0 KiB
1.2-22 latest linux/noble R-4.5 PrivateLR_1.2-22.tar.gz 46.9 KiB
1.2-22 latest source/ R- PrivateLR_1.2-22.tar.gz 9.5 KiB
1.2-22 2026-04-26 source/ R- PrivateLR_1.2-22.tar.gz 9.5 KiB
1.2-22 2026-04-23 source/ R- PrivateLR_1.2-22.tar.gz 9.5 KiB
1.2-22 2026-04-09 windows/windows R-4.5 PrivateLR_1.2-22.zip 49.3 KiB
1.2-22 2025-04-20 source/ R- PrivateLR_1.2-22.tar.gz 9.5 KiB