Cryptographic Approaches for Privacy-Preserving Machine Learning
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摘要: 新一代人工智能技术的特征，表现为借助GPU计算、云计算等高性能分布式计算能力，使用以深度学习算法为代表的机器学习算法，在大数据上进行学习训练，来模拟、延伸和扩展人的智能。不同数据来源、不同的计算物理位置，使得目前的机器学习面临严重的隐私泄露问题，因此隐私保护机器学习(PPM)成为目前广受关注的研究领域。采用密码学工具来解决机器学习中的隐私问题，是隐私保护机器学习重要的技术。该文介绍隐私保护机器学习中常用的密码学工具，包括通用安全多方计算(SMPC)、隐私保护集合运算、同态加密(HE)等，以及应用它们来解决机器学习中数据整理、模型训练、模型测试、数据预测等各个阶段中存在的隐私保护问题的研究方法与研究现状。Abstract: The characteristics of the new generation of artificial intelligence technology are shown as follows: with the help of GPU computing, cloud computing and other high-performance distributed computing capabilities, machine learning algorithms represented by deep learning algorithms are used for learning and training on big data to simulate, extend and expand human intelligence. Different data sources and computing physical locations make the current machine learning face serious privacy leakage problem, so the Privacy Protection of Machine (PPM) Learning has become a widely concerned research area. Using cryptography technology to solve the problem of privacy in machine learning is an important technology to protect the privacy of machine learning. Cryptographic tools used in privacy-preserving machine learning are introduced, such as general Secure Multi-Party Computing (SMPC), privacy protection set operation and Homomorphic Encryption (HE), describes the status and developments applying the tools to solving the problems of privacy protection in various stages of machine learning, such as data processing, model training, model testing, and data prediction.
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表 1 MiniONN效率实验结果
数据集 MNIST CIFAR10 精确度(%) 99.52 91.5 运行时间(s) 320 11686 数据传输(MB) 336.7 1803 #p/h 163840 2524 
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表 2 文献[42]效率分析
通信 计算 存储 用户端 $O\left( {\left( {\lambda + \mu } \right)m + nr} \right)$ $O\left( {m{ {\left( {\lg\left( m \right)} \right)}^2} + \left( {l + 1} \right) \cdot c\left( {r,n} \right)} \right)$ $O\left( {4k\lambda + \mu \left( {m + 3\left\lceil {\dfrac{l}{2} } \right\rceil } \right) + nr} \right)$ 服务器端 $O\left( { {m^2}\mu + nmr + \dfrac{ {ml} }{2} } \right)$ $O\left( {{m^2} + \left( {m - \zeta } \right) \cdot l \cdot c\left( {r,n} \right)} \right)$ $O\left( {mnr + {m^2}\mu + \dfrac{ {ml\mu } }{2} } \right)ght)$
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