SPGC: Integration of Secure Multiparty Computation and Differential Privacy for Gradient Computation on Collaborative Learning

• Item type: Journal(1)
• 公開日: 2022-03-15
• タイトル: SPGC: Integration of Secure Multiparty Computation and Differential Privacy for Gradient Computation on Collaborative Learning
• タイトル / 言語: en
• タイトル: SPGC: Integration of Secure Multiparty Computation and Differential Privacy for Gradient Computation on Collaborative Learning
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集:若手研究者] collaborative learning, privacy-preserving machine learning, secure multiparty computation, differential privacy
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者名: Kazuki, Iwahana; Naoto, Yanai; Jason, Paul Cruz; Toru, Fujiwara
• 著者名(英): Kazuki, Iwahana; Naoto, Yanai; Jason, Paul Cruz; Toru, Fujiwara
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Achieving differential privacy and utilizing secure multiparty computation are the two primary approaches used for ensuring privacy in privacy-preserving machine learning. However, the privacy guarantee by existing integration protocols of both approaches for collaborative learning weakens when more participants join the protocols. In this work, we present Secure and Private Gradient Computation (SPGC), a novel collaborative learning framework with a strong privacy guarantee independent of the number of participants while still providing high accuracy. The main idea of SPGC is to create noise for the differential privacy within secure multiparty computation. We also created an implementation of SPGC and used it in experiments to measure its accuracy and training time. The results show that SPGC is more accurate than a naive protocol based on local differential privacy by up to 5.6%. We experimentally show that the training time increases in proportion to the noise generation and then demonstrate that the privacy guarantee is independent of the number of participants as well as the accuracy evaluation.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.30(2022) (online); DOI http://dx.doi.org/10.2197/ipsjjip.30.209; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Achieving differential privacy and utilizing secure multiparty computation are the two primary approaches used for ensuring privacy in privacy-preserving machine learning. However, the privacy guarantee by existing integration protocols of both approaches for collaborative learning weakens when more participants join the protocols. In this work, we present Secure and Private Gradient Computation (SPGC), a novel collaborative learning framework with a strong privacy guarantee independent of the number of participants while still providing high accuracy. The main idea of SPGC is to create noise for the differential privacy within secure multiparty computation. We also created an implementation of SPGC and used it in experiments to measure its accuracy and training time. The results show that SPGC is more accurate than a naive protocol based on local differential privacy by up to 5.6%. We experimentally show that the training time increases in proportion to the noise generation and then demonstrate that the privacy guarantee is independent of the number of participants as well as the accuracy evaluation.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.30(2022) (online); DOI http://dx.doi.org/10.2197/ipsjjip.30.209; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 63, 号 3, 発行日 2022-03-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764