Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant

[2201.10838] Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Cryptography and Security arXiv:2201.10838 (cs) [Submitted on 26 Jan 2022 ( v1 ), last revised 14 Jul 2026 (this version, v20)] Title: Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant Authors: John Chiang View a PDF of the paper titled Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant, by John Chiang View PDF HTML (experimental) Abstract: Training logistic regression over encrypted data has emerged as a prominent approach to addressing security concerns in recent years. In this paper, we introduce an efficient gradient variant, termed the \textit{quadratic gradient}, which is specifically designed for privacy-preserving logistic regression while remaining equally effective in plaintext optimization. By incorporating this quadratic gradient, we enhance Nesterov's Accelerated Gradient (NAG), Adaptive Gradient (AdaGrad), and Adam algorithms. We evaluate these enhanced algorithms across various datasets, with experimental results demonstrating state-of-the-art convergence rates that significantly outperform traditional first-order gradient methods. Furthermore, we apply the enhanced NAG method to implement homomorphic logistic regression training, achieving comparable performance within only four iterations. The proposed quadratic-gradient approach offers a unified framework that synergizes the advantages of first-order gradient methods and second-order Newton-type methods, suggesting broad applicability to diverse numerical optimization tasks. Subjects: Cryptography and Security (cs.CR) ; Machine Learning (cs.LG) Cite as: arXiv:2201.10838 [cs.CR] (or arXiv:2201.10838v20 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2201.10838 Focus to learn more arXiv-issued DOI via DataCite Submission history From: John Chiang [ view email ] [v1] Wed, 26 Jan 2022 09:44:13 UTC (421 KB) [v2] Sun, 14 Aug 2022 05:01:50 UTC (1,778 KB) [v3] Wed, 29 Mar 2023 12:26:32 UTC (1,777 KB) [v4] Tue, 9 May 2023 18:18:23 UTC (587 KB) [v5] Tue, 9 Jan 2024 00:56:07 UTC (434 KB) [v6] Fri, 7 Jun 2024 03:19:17 UTC (670 KB) [v7] Tue, 6 Aug 2024 03:34:27 UTC (1,234 KB) [v8] Wed, 4 Sep 2024 18:36:30 UTC (1,235 KB) [v9] Sun, 22 Sep 2024 13:34:28 UTC (1,729 KB) [v10] Mon, 2 Dec 2024 10:29:31 UTC (1,729 KB) [v11] Sun, 2 Feb 2025 04:19:36 UTC (1,729 KB) [v12] Fri, 23 May 2025 04:29:47 UTC (1,231 KB) [v13] Sat, 31 May 2025 11:49:42 UTC (1,268 KB) [v14] Sat, 25 Oct 2025 06:26:03 UTC (1,268 KB) [v15] Sat, 28 Feb 2026 10:22:02 UTC (1,277 KB) [v16] Mon, 16 Mar 2026 01:33:42 UTC (1,277 KB) [v17] Wed, 15 Apr 2026 04:55:44 UTC (1,278 KB) [v18] Mon, 1 Jun 2026 12:06:20 UTC (1,287 KB) [v19] Wed, 1 Jul 2026 04:11:41 UTC (1,287 KB) [v20] Tue, 14 Jul 2026 09:02:18 UTC (1,290 KB) Full-text links: Access Paper: View a PDF of the paper titled Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant, by John Chiang View PDF HTML (experimental) TeX Source view license Current browse context: cs.CR < prev | next > new | recent | 2022-01 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar DBLP - CS Bibliography listing | bibtex export BibTeX citation Loading... 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