网络与信息安全学报 ›› 2018, Vol. 4 ›› Issue (8): 1-11.doi: 10.11959/j.issn.2096-109x.2018067

• 综述 •    下一篇

机器学习安全及隐私保护研究进展

宋蕾,马春光(),段广晗   

  1. 哈尔滨工程大学计算机科学与技术学院,黑龙江 哈尔滨 150001
  • 修回日期:2018-07-02 出版日期:2018-08-15 发布日期:2018-10-12
  • 作者简介:宋蕾(1989-),女,黑龙江牡丹江人,哈尔滨工程大学博士生,主要研究方向为机器学习安全与隐私保护、云计算、网络安全。|马春光(1974-),男,黑龙江双城人,哈尔滨工程大学教授、博士生导师,主要研究方向为分布式密码算法与协议、云计算安全与隐私、格密码、机器学习安全与隐私保护。|段广晗(1994-),男,黑龙江海伦人,哈尔滨工程大学博士生,主要研究方向为深度学习、对抗样本、机器学习。
  • 基金资助:
    国家自然科学基金资助项目(61472097)

Machine learning security and privacy:a survey

Lei SONG,Chunguang MA(),Guanghan DUAN   

  1. School of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China
  • Revised:2018-07-02 Online:2018-08-15 Published:2018-10-12
  • Supported by:
    The National Natural Science Foundation of China(61472097)

摘要:

机器学习作为实现人工智能的一种重要方法,在数据挖掘、计算机视觉、自然语言处理等领域得到广泛应用。随着机器学习应用的普及发展,其安全与隐私问题受到越来越多的关注。首先结合机器学习的一般过程,对敌手模型进行了描述。然后总结了机器学习常见的安全威胁,如投毒攻击、对抗攻击、询问攻击等,以及应对的防御方法,如正则化、对抗训练、防御精馏等。接着对机器学习常见的隐私威胁,如训练数据窃取、逆向攻击、成员推理攻击等进行了总结,并给出了相应的隐私保护技术,如同态加密、差分隐私。最后给出了亟待解决的问题和发展方向。

关键词: 机器学习, 安全威胁, 防御技术, 隐私保护

Abstract:

As an important method to implement artificial intelligence,machine learning technology is widely used in data mining,computer vision,natural language processing and other fields.With the development of machine learning,it brings amount of security and privacy issues which are getting more and more attention.Firstly,the adversary model was described according to machine learning.Secondly,the common security threats in machine learning was summarized,such as poisoning attacks,adversarial attacks,oracle attacks,and major defense methods such as regularization,adversarial training,and defense distillation.Then,privacy issues such were summarized as stealing training data,reverse attacks,and membership tests,as well as privacy protection technologies such as differential privacy and homomorphic encryption.Finally,the urgent problems and development direction were given in this field.

Key words: machine learning, security threats, defense technology, privacy

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