Computer Science ›› 2021, Vol. 48 ›› Issue (7): 33-39.doi: 10.11896/jsjkx.201200224
Special Issue: Artificial Intelligence Security
• Artificial Intelligence Security • Previous Articles Next Articles
CHEN Tian-rong, LING Jie
CLC Number:
[1]HA T,DANG T K,DANG T T,et al.Differential Privacy inDeep Learning:An Overview[C]//2019 International Confe-rence on Advanced Computing and Applications (ACOMP).Piscataway,NJ,USA:IEEE,2019:97-102. [2]AHMED S,APRATIM B,MICHEAL B,et al.Updates-Leak:Data Set Inference and Reconstruction Attacks in Online Lear-ning[C]//29th USENIX Security Symposium.Online:USENIX Association,2019:1291-1308. [3]SHOKRI R,STROATI M,SONG C Z,et al.Membership Infe-rence Attacks Against Machine Learning Models[C]//2017 38th IEEE Symposium on Security and Privacy (SP).Los Alamitos,CA,USA:IEEE Computer Society,2017:3-18. [4]DWORK C,KENTHAPADI K,MCSHERRY F,et al.Our data,ourselves:privacy via distributed noise generation[C]//24th Annual International Conference on the Theory and Applications of Cryptographic Techniques Advances in Cryptology(EUROCRYPT 2006).Berlin,Germany:IEEE Computer Socie-ty,2006:486-503. [5]ABADI M,MCMAHANH B,CHU A,et al.Deep learning with differential privacy[C]//Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security(CCS 2016).Vienna,Austria:Association for Computing Machinery,2016:308-318. [6]XIE L Y,LIN K X,WANG S,et al.Differentially Private Gene-rative Adversarial Network[J/OL].http://arxiv.org/abs/1802.06739,2020-5-13. [7]PHAN N,WANG Y,WU X,et al.Differential privacy preservation for deep auto-encoders:An application of human behavior prediction[C]//30th AAAI Conference on Artificial Intelligence(AAAI 2016).Phoenix,AZ,United states:AAAI press,2016:1309-1316. [8]PHAN N,WU X,HU H,et al.Adaptive Laplace mechanism:differential privacy preservation in deep learning[C]//2017 IEEE International Conference on Data Mining (ICDM).Los Alamitos,CA,USA:IEEE Computer Society,2017:385-394. [9]PAPERNOT N,GOODFELLOW I,ABADI M,et al.Semi-supervised knowledge transfer for deep learning from private training data[C]//5th International Conference on Learning Representations(ICLR 2017).Conference Track Proceedings.Toulon,France:ICLR,2017:1024-1040. [10]GANJU K,WANG Q,YANG W,et al.Property inference attacks on fully connected neural networks using permutation invariant representations[C]//Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security(CCS 2018).United States:Association for Computing Machi-nery,2018:619-633. [11]JOON O S,BERNT S,MARIO F.Towards Reverse-Enginee-ring Black-Box Neural Networks[J].Springer Verlag,2017,11700(2017):121-144. [12]SALEM A,YANG Z,HUMBERT M,et al.ML-Leaks:Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models[J/OL].http://arxiv.org/abs/1806.01246,2018-12-14. [13]SHOKRI R,STRONATI M,SONG C,et al.Membership Infe-rence Attacks Against Machine Learning Models[C]//2017 38th IEEE Symposium on Security and Privacy (SP).Los Alamitos,CA,USA:IEEE Computer Society,2017:3-18. [14]WANG B,GONG N.Stealing Hyperparameters in MachineLearning[C]//2018 IEEE Symposium on Security and Privacy (SP).Los Alamitos,CA,USA:IEEE Computer Society,2018:36-52. [15]PHAN N,WU X,DOU D.Preserving differential privacy in convolutional deep belief networks[J].MACH LEARN,2017,106:1681-1704. [16]GONG M,PAN K,XIE Y,et al.Preserving differential privacy in deep neural networks with relevance-based adaptive noise imposition[J].Neural Networks,2020,125:131-141. [17]DONG J S,ROTH A,SU W J,et al.Gaussian Differential Privacy [J/OL].http://arxiv.org/abs/1905.02383,2019-10-08. |
[1] | LENG Dian-dian, DU Peng, CHEN Jian-ting, XIANG Yang. Automated Container Terminal Oriented Travel Time Estimation of AGV [J]. Computer Science, 2022, 49(9): 208-214. |
[2] | NING Han-yang, MA Miao, YANG Bo, LIU Shi-chang. Research Progress and Analysis on Intelligent Cryptology [J]. Computer Science, 2022, 49(9): 288-296. |
[3] | TANG Ling-tao, WANG Di, ZHANG Lu-fei, LIU Sheng-yun. Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy [J]. Computer Science, 2022, 49(9): 297-305. |
[4] | HE Qiang, YIN Zhen-yu, HUANG Min, WANG Xing-wei, WANG Yuan-tian, CUI Shuo, ZHAO Yong. Survey of Influence Analysis of Evolutionary Network Based on Big Data [J]. Computer Science, 2022, 49(8): 1-11. |
[5] | WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang. Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [J]. Computer Science, 2022, 49(8): 12-25. |
[6] | LI Yao, LI Tao, LI Qi-fan, LIANG Jia-rui, Ibegbu Nnamdi JULIAN, CHEN Jun-jie, GUO Hao. Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network [J]. Computer Science, 2022, 49(8): 257-266. |
[7] | ZHANG Guang-hua, GAO Tian-jiao, CHEN Zhen-guo, YU Nai-wen. Study on Malware Classification Based on N-Gram Static Analysis Technology [J]. Computer Science, 2022, 49(8): 336-343. |
[8] | CHEN Ming-xin, ZHANG Jun-bo, LI Tian-rui. Survey on Attacks and Defenses in Federated Learning [J]. Computer Science, 2022, 49(7): 310-323. |
[9] | HUANG Jue, ZHOU Chun-lai. Frequency Feature Extraction Based on Localized Differential Privacy [J]. Computer Science, 2022, 49(7): 350-356. |
[10] | DU Li-jun, TANG Xi-lu, ZHOU Jiao, CHEN Yu-lan, CHENG Jian. Alzheimer's Disease Classification Method Based on Attention Mechanism and Multi-task Learning [J]. Computer Science, 2022, 49(6A): 60-65. |
[11] | LI Ya-ru, ZHANG Yu-lai, WANG Jia-chen. Survey on Bayesian Optimization Methods for Hyper-parameter Tuning [J]. Computer Science, 2022, 49(6A): 86-92. |
[12] | ZHAO Lu, YUAN Li-ming, HAO Kun. Review of Multi-instance Learning Algorithms [J]. Computer Science, 2022, 49(6A): 93-99. |
[13] | YANG Jian-nan, ZHANG Fan. Classification Method for Small Crops Combining Dual Attention Mechanisms and Hierarchical Network Structure [J]. Computer Science, 2022, 49(6A): 353-357. |
[14] | XIAO Zhi-hong, HAN Ye-tong, ZOU Yong-pan. Study on Activity Recognition Based on Multi-source Data and Logical Reasoning [J]. Computer Science, 2022, 49(6A): 397-406. |
[15] | YAO Ye, ZHU Yi-an, QIAN Liang, JIA Yao, ZHANG Li-xiang, LIU Rui-liang. Android Malware Detection Method Based on Heterogeneous Model Fusion [J]. Computer Science, 2022, 49(6A): 508-515. |
|