期刊
IEEE SYSTEMS JOURNAL
卷 16, 期 2, 页码 2032-2043出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSYST.2021.3078637
关键词
Deep learning; Cryptography; Robots; Training; Neurons; Computational modeling; Encryption; Deep learning; multiple keys; privacy-preserving; re-encryption; robot system
类别
资金
- National Natural Science Foundation of China [U19B2021, 61972457]
- National Cryptography Development Fund [MMJJ20180111]
- Key Research and Development Program of Shaanxi [2020ZDLGY08-04]
- Key Technologies R&D Program of He'nan Province [212102210084, 192102210295]
- Innovation Scientists and Technicians Troop Construction Projects of Henan Province
The article proposes a privacy-preserving deep learning model with homomorphic re-encryption and secure calculation tools for robot systems, addressing privacy leakage issues, improving efficiency, and preserving the privacy of input data, training model, and inference results.
The robot system is a significant application that has attracted great attention, and deep learning is a powerful feature extraction technology that has achieved significant breakthroughs in many fields, especially in robot systems. However, the required massive dataset for a deep learning model in a robot system easily leads to privacy leakage. There have been few reports on privacy-preserving deep learning models, and none on multikeys, in robot systems. Existing privacy-preserving deep learning schemes in multiple keys have low efficiency and high interactions in non-robotic environments. To address these issues, this article proposes a privacy-preserving deep learning model with homomorphic re-encryption (PDLHR) and secure calculation tools in a robot system. The proposed re-encryption scheme is based on the Bresson-Catalano-Pointcheval (BCP) cryptosystem, which solves the multiple keys question, keeps the homomorphic nature, and is more simplified than the existing re-encryption scheme based on the BCP cryptosystem. The secure calculation tools are designed to realize efficient ciphertext computations. Compared to the previous work, PDLHR decreases the interactions in the decryption process, improves the ciphertext training efficiency, and preserves the privacy of input data, training model, and inference results. Security analysis and performance evaluations demonstrate that the proposed scheme realizes security, efficiency, and effectiveness with low communication and computation costs.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据