文献类型：期刊文献

英文题名：Privacy-Preserving Classification on Deep Learning with Exponential Mechanism

作者：Ju, Quan[1];Xia, Rongqing[1];Li, Shuhong[1];Zhang, Xiaojian[1]

第一作者：Ju, Quan

通讯作者：Li, SH[1]

机构：[1]Henan Univ Econ & Law, Sch Comp & Informat Engn, Zhengzhou 450046, Peoples R China

第一机构：河南财经政法大学计算机与信息工程学院

通讯机构：[1]corresponding author), Henan Univ Econ & Law, Sch Comp & Informat Engn, Zhengzhou 450046, Peoples R China.|[1048412]河南财经政法大学计算机与信息工程学院;[10484]河南财经政法大学;

年份：2024

卷号：17

期号：1

外文期刊名：INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS

收录：;EI(收录号：20240915652431);Scopus(收录号：2-s2.0-85185959322);WOS:【SCI-EXPANDED(收录号:WOS:001169565800001)】；

语种：英文

外文关键词：Exponential mechanism; PATE; Deep learning; Differential privacy

摘要：How to protect the privacy of training data in deep learning has been the subject of increasing amounts of related research in recent years. Private Aggregation of Teacher Ensembles (PATE) uses transfer learning and differential privacy methods to provide a broadly applicable data privacy framework in deep learning. PATE combines the Laplacian mechanism and the voting method to achieve deep learning privacy classification. However, the Laplacian mechanism may greatly distort the histogram vote counts of each class. This paper proposes a novel exponential mechanism with PATE to ensure the privacy protection. This proposed method improves the protection effect and accuracy through the screening algorithm and uses the differential privacy combination theorems to reduce the total privacy budget. The data-dependent analysis demonstrates that the exponential mechanism outperforms the original Laplace mechanism. Experimental results show that the proposed method can train models with improved accuracy while requiring a smaller privacy budget when compared to the original Pate framework.