A Multi-Lossless Linear Model Learning Method with Data Privacy-Preserving

doi:10.3901/JME.2023.12.017

Abstract

Abstract: With the development of industrial big data technologies, manufacturing enterprises collect and analyse production data to obtain optimization methods of forecasting and diagnosis. However, manufacturing companies are constrained by technical bottlenecks such as modelling and computing power, which make it difficult to analyse data efficiently. Manufacturing enterprises need to bear the risk of information leakage, and it is also difficult to guarantee that model performance is lossless, when cooperating with other participants. For these scenario problems, a multi-lossless linear model learning method based on data privacy preserving is proposed. Firstly, a multi-party collaborative computing framework is built and the one-way data encryption algorithm is designed to protect the privacy of the data. Each collaborator trains the linear model separately based on the encrypted data. Secondly, the study analyses the association properties of the linear model with the dataset, proposing a lossless aggregation algorithm for linear models. Finally, the method is validated on the typical industrial scenario dataset. The experimental results show that the proposed framework can obtain global models with lossless performance and achieve privacy security for the data holders.

Key words: industrial big data, privacy-preserving machine learning, federated learning, data analysis

CLC Number:

TP301

HUA Feng, WANG Yasen, JIN Junyang, YUAN Ye. A Multi-Lossless Linear Model Learning Method with Data Privacy-Preserving[J]. Journal of Mechanical Engineering, 2023, 59(12): 17-27.

References

[1] DING Han,GAO R X,ISAKSSON A J,et al. State of AI-based monitoring in smart manufacturing and introduction to focused section[J]. IEEE/ASME Transactions on Mechatronics,2020,25(5):2143-2154.
[2] YUAN Ye,MA Guijun,CHENG Cheng,et al. A general end-to-end diagnosis framework for manufacturing systems[J]. National Science Review,2020,7(2):418-429.
[3] CHENG Cheng,MA Guijun,ZHANG Yong,et al. A deep learning-based remaining useful life prediction approach for bearings[J]. IEEE/ASME Transactions on Mechatronics,2020,25(3):1243-1254.
[4] ZHAO Huimin,LIU Haodong,JIN Yang,et al. Feature extraction for data-driven remaining useful life prediction of rolling bearings[J]. IEEE Transactions on Instrumentation and Measurement,2021,70:1-10.
[5] YUAN Ye,TANG Xiuchuan,ZHOU Wei,et al. Data driven discovery of cyber physical systems[J]. Nature Communications,2019,10(1):1-9.
[6] JIN Yaochu,WANG Handing,CHUGH T,et al. Data-driven evolutionary optimization:An overview and case studies[J]. IEEE Transactions on Evolutionary Computation,2018,23(3):442-458.
[7] REN Hao,LI Hongwei,DAI Yuanshun,et al. Querying in internet of things with privacy preserving:Challenges, solutions and opportunities[J]. IEEE Network,2018,32(6):144-151.
[8] WANG Junliang,ZHENG Peng,LV Youlong,et al. Fog-IBDIS:Industrial big data integration and sharing with fog computing for manufacturing systems[J]. Engineering,2019,5(4):662-670.
[9] 李尤慧子,殷昱煜,高洪皓,等. 面向隐私保护的非聚合式数据共享综述[J]. 通信学报,2021,42(6):195-212. LI Youhuizi,YIN Yuyu,GAO Honghao,et al. Survey on privacy protection in non-aggregated data sharing[J]. Journal on Communications,2021,42(6):195-212.
[10] LI Hongwei,LIU Dongxiao,DAI Yuanshun,et al. Engineering searchable encryption of mobile cloud networks:When QoE meets QoP[J]. IEEE Wireless Communications,2015,22(4):74-80.
[11] LI Hongwei,YANG Yi,DAI Yuanshun,et al. Achieving secure and efficient dynamic searchable symmetric encryption over medical cloud data[J]. IEEE Transactions on Cloud Computing,2017,8(2):484-494.
[12] MOHASSEL P,ZHANG Yupeng. Secureml:A system for scalable privacy-preserving machine learning[C]//IEEE symposium on security and privacy,May 22-24,2017,San Jose,California. IEEE,2017:19-38.
[13] BONAWITZ K,IVANOV V,KREUTER B,et al. Practical secure aggregation for privacy-preserving machine learning[C]//Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security,October 30-November 3,2017,Dallas Texas USA. New York:ACM,2017:1175-1191.
[14] Al-RUBAIE M,CHANG J M. Privacy-preserving machine learning:Threats and solutions[J]. IEEE Security & Privacy,2019,17(2):49-58.
[15] LI Ping,LI Tong,YE Heng,et al. Privacy-preserving machine learning with multiple data providers[J]. Future Generation Computer Systems,2018,87:341-350.
[16] MCMAHAN B,MOORE E,RAMAGE D,et al. Communication-efficient learning of deep networks from decentralized data[C]//Artificial Intelligence and Statistics,20-22 April 2017,Fort Lauderdale,USA. JMLR:W&CP volume 54,2017:1273-1282.
[17] BONAWITZ K,EICHNER H,GRIESKAMP W,et al. Towards federated learning at scale:System design[J]. Proceedings of Machine Learning and Systems,2019,1:374-388.
[18] KONECNY J,MCMAHAN H B,YU F X,et al. Federated learning:Strategies for improving communication efficiency[J]. arXiv preprint arXiv:1610.05492,2016.
[19] YANG Qiang,LIU Yang,CHENG Yong,et al. Federated learning[C]//Synthesis Lectures on Artificial Intelligence and Machine Learning, Switzerland, 2019.
[20] YANG Qiang,LIU Yang,CHEN Tianjian,et al. Federated machine learning:Concept and applications[J]. ACM Transactions on Intelligent Systems and Technology,2019,10(2):1-19.
[21] RIVEST R L,ADLEMAN L,DERTOUZOS M L. On data banks and privacy homomorphisms[J]. Foundations of secure computation,1978,4(11):169-180.
[22] YI Xun,PAULET R,BERTINO E. Homomorphic encryption[M]//Homomorphic encryption and applications. Springer,Cham,2014:27-46.
[23] FAN Junfeng,VERCAUTEREN F. Somewhat practical fully homomorphic encryption[J]. Cryptology ePrint Archive,2012.
[24] BRAKERSKI Z,GENTRY C,VAIKUNTANATHAN V. (Leveled) fully homomorphic encryption without bootstrapping[J]. ACM Transactions on Computation Theory,2014,6(3):1-36.
[25] ACAR A,AKSU H,ULUAGAC A S,et al. A survey on homomorphic encryption schemes:Theory and implementation[J]. ACM Computing Surveys,2018,51(4):1-35.
[26] DWORK C,MCSHERRY F,NISSIM K,et al. Calibrating noise to sensitivity in private data analysis[C]//Theory of cryptography conference,March 4-72006,New York,USA. Berlin,Heidelberg:Springer,2006:265-284.
[27] DWORK C,ROTH A. The algorithmic foundations of differential privacy[J]. Foundations and Trends in Theoretical Computer Science,2014,9(3-4):211-407.
[28] ABAD M,CHU A,GOODFELLOW I,et al. Deep learning with differential privacy[C]//Proceedings of the ACM SIGSAC conference on computer and communications security,October 24-282016,Vienna,Austria. New York:ACM,2016:308-318.
[29] ZHANG Jun,ZHANG Zhenjie,XIAO Xiaokui,et al. Functional mechanism:regression analysis under differential privacy[J]. arXiv preprint arXiv:1208.0219, 2012.