| 谭作文,张连福.机器学习隐私保护研究综述.软件学报,2020,31(7):0 |
| 机器学习隐私保护研究综述 |
| Survey on Privacy Preserving Techniques for Machine Learning |
| 投稿时间：2019-09-10 修订日期：2020-03-20 |
| DOI：10.13328/j.cnki.jos.006052 |
| 中文关键词: 机器学习 隐私保护 差分隐私 同态加密 安全多方计算 |
| 英文关键词:machine learning privacy-preserving differential privacy homomorphic encryption secure multiparty computation |
| 基金项目:国家自然科学基金（61862028，61702238）；江西省自然科学基金（20181BAB202016）；江西省教育厅科技项目（GJJ160430）；江西省教育厅青年科技项目（GJJ180288） |
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| 中文摘要: |
| 机器学习已成为大数据、物联网和云计算等领域核心技术.机器学习模型训练需要大量数据，这些数据通常通过众包方式收集，里面含有大量隐私数据包括个人身份信息（如电话号码、身份证号等）、敏感信息（如金融财务、医疗健康等信息）.如何低成本且高效地保护这些数据是一个重要的问题.介绍了机器学习及其隐私定义和隐私威胁，重点对机器学习隐私保护主流技术的工作原理和突出特点进行了阐述，并分别按照差分隐私、同态加密和安全多方计算等机制对机器学习隐私保护领域的研究成果进行了综述.在此基础上，对比分析了机器学习不同隐私保护机制的主要优缺点.最后，对机器学习隐私保护的发展趋势进行展望，并提出了该领域未来可能的研究方向. |
| 英文摘要: |
| Machine learning has become a core technology in areas such as big data, Internet of things and cloud computing. Training machine learning models requires a large amount of data, which is often collected by means of crowdsourcing and contains a large number of private data including personally identifiable information (such as phone number, id number, etc.) and sensitive information (such as financial data, health care, etc.). How to protect these data with low cost and high efficiency is an important issue. This paper first introduces the concept of machine learning, explains various definitions of privacy in machine learning and demonstrates all kinds of privacy threats encountered in machine learning, then continues to elaborate on the working principle and outstanding features of the mainstream technology of machine learning privacy protection. According to differential privacy, homomorphic encryption and secure multi-party computing, the research achievements in the field of machine learning privacy protection are summarized respectively. On this basis, the paper comparatively analyzes the main advantages and disadvantages of different mechanisms of privacy preserving for machine learning. Finally, the developing trend of privacy preserving for machine learning is prospected, and the possible research directions in this field are proposed. |
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