[1] |
SWEENEY L . K-anonymity:a model for protecting privacy[J]. International Journal of Uncertainty,Fuzziness and KnowledgeBased Systems, 2002,10(5): 557-570.
|
[2] |
MACHANAVAJJHALA A , KIFER D , GEHRKE J ,et al. L-diversity:privacy beyond k-anonymity[J]. ACM Transactions on Knowledge Discovery from Data (TKDD), 2007,1(1):3.
|
[3] |
LI N , LI T , VENKATASUBRAMANIAN S . T-closeness:privacy beyond k-anonymity and l-diversity[C]// IEEE 23rd International Conference on Data Engineering. 2007: 106-115.
|
[4] |
TRIASTCYN A , FALTINGS B . Generating artificial data for private deep learning[C]// Proceedings of the PAL:Privacy-Enhancing Artificial Intelligence and Language Technologies,AAAI Spring Symposium Series. 2019.
|
[5] |
GILAD-BACHRACH R , DOWLIN N , LAINE K ,et al. CryptoNets:applying neural networks to encrypted data with high throughput and accuracy[C]// International Conference on Machine Learning. 2016: 201-210.
|
[6] |
HESAMIFARD E , TAKABI H , GHASEMI M . Cryptodl:deep neural networks over encrypted data[J]. arXiv preprint arXiv:1711.05189, 2017
|
[7] |
SANYAL A , KUSNER M , GASCON A ,et al. TAPAS:tricks to accelerate (encrypted) prediction as a service[C]// International Conference on Machine Learning. 2018: 4490-4499.
|
[8] |
BOURSE F , MINELLI M , MINIHOLD M ,et al. Fast homomorphic evaluation of deep discretized neural networks[J]. IACR Cryptology ePrint Archive, 2017
|
[9] |
MOHASSEL P , ZHANG Y . SecureML:a system for scalable privacy-preserving machine learning[C]// 2017 IEEE Symposium on Security and Privacy (SP). 2017: 19-38.
|
[10] |
GRAEPEL , THORE , KRISTIN ,et al. Ml confidential:machine learning on encrypted data[C]// Information Security and Cryptology–ICISC 2012. 2012: 1-21.
|
[11] |
ASLETT L J M , ESPERAN?A P M , HOLMES C . A review of homomorphic encryption and software tools for encrypted statistical machine learning[J]. Stat,2015, 1050:26.
|
[12] |
宋蕾, 马春光, 段广晗 . 机器学习安全及隐私保护研究进展[J]. 网络与信息安全学报, 2018,4(8): 1-11.
|
|
SONG L , MA C G , DUAN G H . Machine learning security and privacy:a survey[J]. Chinese Journal of Network and Information Security, 2018,4(8): 1-11.
|
[13] |
PARK N , MOHAMMADI M , GORDE K ,et al. Data synthesis based on generative adversarial networks[J]. Proceedings of the VLDB Endowment, 2018,11(10): 1071-1083.
|
[14] |
REED S , AKATA Z , YAN X ,et al. Generative adversarial text to image synthesis[C]// International Conference on Machine Learning. 2016: 1060-1069.
|
[15] |
YU L , ZHANG W , WANG J ,et al. SeqGAN:sequence generative adversarial nets with policy gradient[C]// Thirty-First AAAI Conference on Artificial Intelligence. 2017.
|
[16] |
MAYILVELKUMAR P , KARTHIKEYAN M . L-diversity on k-anonymity with external database for improving privacy preserving data publishing[J]. International Journal of Computer Applications, 2012,54(14): 7-13.
|
[17] |
WANG Q , XU Z W , QU S Z ,et al. An enhanced k-anonymity model against homogeneity attack[J]. Journal of Software, 2011: 1945-1952.
|
[18] |
BOS J W , LAUTER K , LOFTUS J ,et al. Improved security for a ring-based fully homomorphic encryption scheme[C]// IMA International Conference on Cryptography and Coding. 2013: 45-64.
|
[19] |
DWORK C , . Differential privacy:a survey of results[C]// International Conference on Theory and Applications of Models of Computation. 2008: 1-19.
|
[20] |
PHAN N , WANG Y , WU X T ,et al. Differential privacy preservation for deep auto-encoders:an application of human behavior prediction[C]// AAAI Conference on Artificial Intelligence. 2016.
|
[21] |
ABADI M , CHU A , GOODFELLOW I ,et al. Deep learning with differential privacy[C]// Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. 2016: 308-318.
|
[22] |
CHAUDHURI K , MONTELEONI C . Privacy-preserving logistic regression[C]// Advances in Neural Information Processing Systems. 2009: 289-296.
|
[23] |
PAPERNOT N , ABADI M , ERLINGSSON U ,et al. Semi-supervised knowledge transfer for deep learning from private training data[J]. Stat,2017, 1050:3.
|
[24] |
WANG J , ZHANG J G , BAO W D ,et al. Not just privacy:improving performance of private deep learning in mobile cloud[C]// Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). 2018.
|
[25] |
GOODFELLOW I , . Generative adversarial nets[C]// NIPS. 2014: 2672-2680.
|
[26] |
ARJOVSKY M , CHINTALA S , BOTTOU L . Wasserstein generative adversarial networks[C]// Proceedings of the 34th International Conference on Machine Learning 70. 2017: 214-223.
|
[27] |
RADFORD A , METZ L , CHINTALA S . Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv preprint arXiv:1511.06434, 2015
|
[28] |
CHEN X , DUAN Y , HOUTHOOFT R ,et al. InfoGAN:interpretable representation learning by information maximizing generative adversarial nets[C]// Proceedings of the 2016Neural Information Processing Systems of Information Technology IMEC. 2016: 2172-2180
|
[29] |
LEDIG C , THEIS L , HUSZáR F ,et al. Photo-realistic single image super-resolution using a generative adversarial net-work[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 4681-4690.
|
[30] |
ZHU J Y , KR?HENBüHL P , SHECHTMAN E ,et al. Generative visual manipulation on the natural image manifold[C]// European Conference on Computer Vision. 2016: 597-613.
|
[31] |
ISOLA P , ZHU J Y , ZHOU T ,et al. Image-to-image translation with conditional adversarial networks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 1125-1134.
|
[32] |
KIM Y , . Convolutional neural networks for sentence classifica-tion[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014: 1746-1751.
|
[33] |
HOCHREITER S , URGEN SCHMIDHUBER J , ELVEZIA C . Long short-term memory[J]. Neural Computation, 1997,9(8): 1735-1780.
|
[34] |
MIRZA M , OSINDERO S . Conditional generative adversarial Nets[J]. arXiv preprint arXiv:1411.1784, 2014
|
[35] |
LI Y , PAN Q , WANG S ,et al. A generative model for category text generation[J]. Information Sciences, 2018,450: 301-315.
|
[36] |
SHOKRI R , STRONATI M , SONG C ,et al. Membership inference attacks against machine learning models[C]// 2017 IEEE Symposium on Security and Privacy (SP). 2017: 3-18.
|
[37] |
YouTube spam collection data set[EB]. 2007.
|
[38] |
MIRONOV , RéNYI , . Differential privacy[C]// 2017 IEEE 30th Computer Security Foundations Symposium (CSF). 2017: 263-275.
|
[39] |
CARLINI N , LIU C , ERLINGSSON ú ,et al. The secret sharer:evaluating and testing unintended memorization in neural networks[C]// 28th USENIX Security Symposium. 2019: 267-284.
|
[40] |
DWORK C , FELDMAN V , HARDT M ,et al. The reusable holdout:preserving validity in adaptive data analysis[J]. Science, 2015,349(6248): 636-638.
|