Differentially Private Learning with Grouped Gradient Clipping

Author:

Liu Haolin1,Li Chenyu1,Liu Bochao1,Wang Pengju2,Ge Shiming2,Wang Weiping3

Affiliation:

1. Institute of Information Engineering, Chinese Academy of Sciences, CN and School of Cybersecurity, University of Chinese Academy of Sciences, China

2. Institute of Information Engineering, Chinese Academy of Sciences, CN

3. Institute of Information Engineering, Chinese Academy of Sciences, China

Funder

National Natural Science Foundation of China

Publisher

ACM

Reference43 articles.

1. Martin Abadi , Andy Chu , Ian Goodfellow , H.  Brendan McMahan , Ilya Mironov , Kunal Talwar , and Li Zhang . 2016 . Deep Learning with Differential Privacy. In ACM SIGSAC Conference on Computer and Communications Security. 308––318 . Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016. Deep Learning with Differential Privacy. In ACM SIGSAC Conference on Computer and Communications Security. 308––318.

2. Eugene Bagdasaryan Omid Poursaeed and Vitaly Shmatikov. 2019. Differential Privacy Has Disparate Impact on Model Accuracy. In Advances in Neural Information Processing Systems Vol. 32. 15479–15488. Eugene Bagdasaryan Omid Poursaeed and Vitaly Shmatikov. 2019. Differential Privacy Has Disparate Impact on Model Accuracy. In Advances in Neural Information Processing Systems Vol. 32. 15479–15488.

3. Raef Bassily , Adam Smith , and Abhradeep Thakurta . 2014 . Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds. In IEEE 55th Annual Symposium on Foundations of Computer Science. 464–473 . Raef Bassily, Adam Smith, and Abhradeep Thakurta. 2014. Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds. In IEEE 55th Annual Symposium on Foundations of Computer Science. 464–473.

4. Zhiqi Bu , Jinshuo Dong , Qi Long , and Su Weijie . 2020. Deep learning with Gaussian differential privacy. Harvard data science review23 ( 2020 ), 1–48. Zhiqi Bu, Jinshuo Dong, Qi Long, and Su Weijie. 2020. Deep learning with Gaussian differential privacy. Harvard data science review23 (2020), 1–48.

5. Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds

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