计算机科学 ›› 2023, Vol. 50 ›› Issue (5): 372-381.doi: 10.11896/jsjkx.220300239
赵敏1,2,3, 田有亮1,2,3, 熊金波1,2,4, 毕仁万4, 谢洪涛5
ZHAO Min1,2,3, TIAN Youliang1,2,3, XIONG Jinbo1,2,4, BI Renwan4, XIE Hongtao5
摘要： 针对云环境下数据隐私泄露与基于同态加密的隐私保护神经网络中精度不足的问题,文中提出了一种双服务器协作的隐私保护神经网络训练(PPNT)方案,在云服务器协同训练过程中实现了对数据传输、计算过程及模型参数的隐私保护。首先,为避免使用多项式近似方法实现指数和比较等非线性函数,并提高非线性函数的计算精度,基于Paillier半同态加密方案和加法秘密共享技术设计了一系列基础安全计算协议;其次,在已设计的安全计算协议基础上,构造了神经网络中的全连接层、激活层、Softmax层及反向传播相应的安全计算协议,以实现PPNT方案;最后,通过理论与安全性分析,证明了PPNT方案的正确性及安全性。性能实验结果显示,与PPMLaaS方案相比,PPNT方案的模型精度提高了1.7%,且在安全计算过程中支持客户端离线。
中图分类号:
| [1]MA Z,LIU Y,LIU X,et al.Lightweight privacy-preserving ensemble classification for face recognition[J].IEEE Internet of Things Journal,2019,6(3):5778-5790. [2]LUO X,LI L,WAN H,et al.Phone keypad voice recognition:an integrated experiment for digital signal processing education[C]//Proceedings of the 2020 IEEE Frontiers in Education Conference.Piscataway:IEEE Press,2020:1-4. [3]LI Z Y,GUI X L,GU Y J,et al.Survey on homomorphic encryption algorithm and its application in the privacy-preserving for cloud computing[J].Journal of Software,2018,29(7):1830-1851. [4]TAN Z W,ZHANG L F.Survey on privacy preserving techniques for machine learning[J].Journal of Software,2020,31(7):2127-2156. [5]GILAD-BACHRACH R,DOWLIN N,LAINE K,et al.Cryp-tonets:applying neural networks to encrypted data with high throughput and accuracy[C]//International Conference on Machine Learning.New York:ACM Press,2016:201-210. [6]HESAMIFARD E,TAKABI H,GHASEMI M.Cryptodl:Deep neural networks over encrypted data[J].arXiv:1711.05189,2017. [7]CHOU E,BEAL J,LEVY D,et al.Faster cryptonets:leveraging sparsity for real-world encrypted inference[J].arXiv:1811.09953,2018. [8]CHABANNE H,DE W A,MILGRAM J,et al.Privacy-preserving classification on deep neural network[J／OL].Cryptology ePrint Archive,2017,1-35.http://eprint.iacr.org/2017/035. [9]JUVEKAR C,VALKUNTANATHAN V,CHANDRAKASAN A.{GAZELLE}:A low latency framework for secure neural network inference[C]//27th USENIX Security Symposium({USENIX} Security 18).Berkeley:USENIX Association,2018:1651-1669. [10]BADAWI A,CHAO J,JIE L,et al.Towards the alexnet mo-ment for homomorphic encryption:hcnn,the first homomorphic cnn on encrypted data with gpus[J].IEEE Transactions on Emerging Topics in Computing,2021,9(3):1330-1343. [11]HAN K,HONG S,CHEON J H,et al.Logistic regression onhomomorphic encrypted data at scale[C]//Proceedings of the AAAI Conference on Artificial Intelligence.Menlo Park:AAAI Press,2019:9466-9471. [12]BRAKERSKI Z,GENTRY C,VAIKUNTANATHAN V.(Le-veled) fully homomorphic encryption without bootstrapping[J].ACM Transactions on Computation Theory(TOCT),2014,6(3):1-36. [13]ZHANG Q,WANG C,WU H,et al.GELU-Net:a globally encrypted,locally unencrypted deep neural network for privacy-preserved learning[C]//Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence.Stockholm:IJCAI.2018:3933-3939. [14]BOURSE F,MINELLI M,MINIHOLD M,et al.Fast homomorphic evaluation of deep discretized neural networks[C]//Annual International Cryptology Conference.Berlin:Springer,2018:483-512. [15]CHILLOTTI I,GAMA N,GEORGIEVA M,et al.Faster fully homomorphic encryption:Bootstrapping in less than 0.1 seconds[C]//International Conference on the Theory and Application of Cryptology and Information Security.Berlin:Springer,2016:3-33. [16]HESAMIFARD E,TAKABI H,GHASEMI M,et al.Privacy-preserving machine learning in cloud[C]//Proceedings of the 2017 on Cloud Computing Security Workshop.New York:ACM Press,2017:39-43. [17]LOU Q,FENG B,CHARLES F G,et al.Glyph:fast and accurately training deep neural networks on encrypted data[J/OL].Advances in Neural Information Processing Systems,2020,33:9193-9202.https://proceedings.neurips.cc/paper/2020/hash/685ac8cadc1be5ac98da9556bc1c8d9e-Abstract.html. [18]PAILLIER P.Public-key cryptosystems based on composite degree residuosity classes[C]//Proceedings of the International Conference on the Theory and Dpplications of Cryptographic Techniques.Berlin:Springer,1999:223-238. [19]SHAMIR A.How to share a secret[J].Communications of the ACM,1979,22(11):612-613. [20]LIU Y,MA Z,LIU X,et al.Privacy-preserving object detection for medical images with faster R-CNN[J／OL].IEEE Transactions on Information Forensics and Security,2022,17:69-84.https://doi.org/10.1109/TIFS.2019.2946476. [21]XIONG J B,BI R W,TIAN Y L,et al.Towards lightweight,privacy-preserving cooperative object classification for connected autonomous vehicles[J].IEEE Internet of Things Journal,2021,9(4):2787-2801. [22]HUANG K,LIU X,FU S,et al.A lightweight privacy-preserving CNN feature extraction framework for mobile sensing[J].IEEE Transactions on Dependable and Secure Computing,2019,18(3):1441-1455. [23]XIONG J B,ZHOU Y J,BI R W,et al.Towards edge-collaborative,lightweight and privacy-preserving classification framework[J].Journal on Communications,2022,43(1):127-137. [24]MA Z,LIU Y,LIU X,et al.Privacy-preserving outsourcedspeech recognition for smart IoT devices[J].IEEE Internet of Things Journal,2019,6(5):8406-8420. [25]BI R W,CHEN Q X,XIONG J B,et al.Design method of secure computing protocol for deep neural network[J].Chinese Journal of Network and Information Security,2020,6(4):130-139. [26]WAGH S,TOPLE S,BENHAMOUDA F,et al.Falcon:honest-majority maliciously secure framework for private deep learning[J].Privacy Enhancing Technologies,2021,2021(1):188-208. [27]BOGDANOV D,NIITSOO M,TOFT T,et al.High-perfor-mance secure multi-party computation for data mining applications[J].International Journal of Information Security,2012,11(6):403-418. [28]XIONG J,BI R,ZHAO M,et al.Edge-assisted privacy-preserving raw data sharing framework for connected autonomous vehicles[J].IEEE Wireless Communications,2020,27(3):24-30. [29]XIONG J B,BI R W,CHEN Q X,et al.Towards edge-collaborative,lightweight and secure region proposal network[J].Journal on Communications,2020,41(10):188-201. [30]HE K,ZHANG X,REN S,et al.Delving deep into rectifiers:surpassing human-level performance on imagenet classification[C]//Proceedings of the IEEE International Conference on Computer Vision.Los Alamitos:IEEE Computer Society,2015:1026-1034. [31]MOHASSEL P,ZHANG Y.Secureml:a system for scalable privacy-preserving machine learning[C]//2017 IEEE Symposium on Security and Privacy(SP).Piscataway:IEEE Press,2017:19-38. [32]HESAMIFARD E,TAKABI H,GHASEMI M,et al.Privacy-preserving machine learning as a service[J].Proceedings on Privacy Enhancing Technologies,2018,2018(3):123-142. [33]LIU J,JUUTI M,LU Y,et al.Oblivious neural network predictions via minionn transformations[C]//Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security.New York:ACM Press,2017:619-631. |
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