隱私保護(hù)機(jī)器學(xué)習(xí)的密碼學(xué)方法
doi: 10.11999/JEIT190887
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山東大學(xué)軟件學(xué)院 濟(jì)南 250101
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山東師范大學(xué)信息科學(xué)與工程學(xué)院 濟(jì)南 250358
基金項目: 國家自然科學(xué)基金(61632020, 61572294)；山東省自然科學(xué)基金(ZR2017MF021)；山東省科技重大創(chuàng)新工程項目(2018CXGC0702)；山東半島國家自主創(chuàng)新示范區(qū)發(fā)展建設(shè)項目(S190101010001)
Cryptographic Approaches for Privacy-Preserving Machine Learning
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School of Software, Shandong University, Jinan 250101, China
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School of Information Science and Technology, Shandong Normal University, Jinan 250358, China
Funds: The National Natural Science Foundation of China (61632020, 61572294); The Natural Science Foundation of Shandong Province (ZR2017MF021); The Major Innovation Project of Science and Technology of Shandong Province (2018CXGC0702); The Funds Project of National Independent Innovation Demonstration Zone in Shandong Peninsula (S190101010001)
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摘要: 新一代人工智能技術(shù)的特征，表現(xiàn)為借助GPU計算、云計算等高性能分布式計算能力，使用以深度學(xué)習(xí)算法為代表的機(jī)器學(xué)習(xí)算法，在大數(shù)據(jù)上進(jìn)行學(xué)習(xí)訓(xùn)練，來模擬、延伸和擴(kuò)展人的智能。不同數(shù)據(jù)來源、不同的計算物理位置，使得目前的機(jī)器學(xué)習(xí)面臨嚴(yán)重的隱私泄露問題，因此隱私保護(hù)機(jī)器學(xué)習(xí)(PPM)成為目前廣受關(guān)注的研究領(lǐng)域。采用密碼學(xué)工具來解決機(jī)器學(xué)習(xí)中的隱私問題，是隱私保護(hù)機(jī)器學(xué)習(xí)重要的技術(shù)。該文介紹隱私保護(hù)機(jī)器學(xué)習(xí)中常用的密碼學(xué)工具，包括通用安全多方計算(SMPC)、隱私保護(hù)集合運算、同態(tài)加密(HE)等，以及應(yīng)用它們來解決機(jī)器學(xué)習(xí)中數(shù)據(jù)整理、模型訓(xùn)練、模型測試、數(shù)據(jù)預(yù)測等各個階段中存在的隱私保護(hù)問題的研究方法與研究現(xiàn)狀。
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關(guān)鍵詞:
- 隱私保護(hù)機(jī)器學(xué)習(xí) /
- 安全多方計算 /
- 同態(tài)加密 /
- 隱私保護(hù)集合求交
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. -
表 1 MiniONN效率實驗結(jié)果
數(shù)據(jù)集 MNIST CIFAR10 精確度(%) 99.52 91.5 運行時間(s) 320 11686 數(shù)據(jù)傳輸(MB) 336.7 1803 #p/h 163840 2524 
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表 2 文獻(xiàn)[42]效率分析
通信 計算 存儲 用戶端 $O\left( {\left( {\lambda + \mu } \right)m + nr} \right)$ $O\left( {m{ {\left( {\lg\left( m \right)} \right)}^2} + \left( {l + 1} \right) \cdot c\left( {r,n} \right)} \right)$ $O\left( {4k\lambda + \mu \left( {m + 3\left\lceil {\dfrac{l}{2} } \right\rceil } \right) + nr} \right)$ 服務(wù)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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