ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (10): 2066-2085.doi: 10.7544/issn1000-1239.2020.20200426

Special Issue: 2020密码学与数据隐私保护研究专题

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Security Issues and Privacy Preserving in Machine Learning

Wei Lifei, Chen Congcong, Zhang Lei, Li Mengsi, Chen Yujiao, Wang Qin   

  1. (College of Information Technology, Shanghai Ocean University, Shanghai 201306)
  • Online:2020-10-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61972241, 61802248, 61672339), the Natural Science Foundation of Shanghai (18ZR1417300), and the Luo Zhaorao Science and Technology Innovation Fund of Shanghai Ocean University (A1-2004-20-201312).

Abstract: In recent years, machine learning has developed rapidly, and it is widely used in the aspects of work and life, which brings not only convenience but also great security risks. The security and privacy issues have become a stumbling block in the development of machine learning. The training and inference of the machine learning model are based on a large amount of data, which always contains some sensitive information. With the frequent occurrence of data privacy leakage events and the aggravation of the leakage scale annually, how to make sure the security and privacy of data has attracted the attention of the researchers from academy and industry. In this paper we introduce some fundamental concepts such as the adversary model in the privacy preserving of machine learning and summarize the common security threats and privacy threats in the training and inference phase of machine learning, such as privacy leakage of training data, poisoning attack, adversarial attack, privacy attack, etc. Subsequently, we introduce the common security protecting and privacy preserving methods, especially focusing on homomorphic encryption, secure multi-party computation, differential privacy, etc. and compare the typical schemes and applicable scenarios of the three technologies. At the end, the future development trend and research direction of machine learning privacy preserving are prospected.

Key words: machine learning, privacy preserving, security threat, secure multi-party computation, homomorphic encryption, differential privacy

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