Concurrent Composition Theorems for Differential Privacy

被引:1
作者
Vadhan, Salil [1 ]
Zhang, Wanrong [1 ]
机构
[1] Harvard Univ, Cambridge, MA 02138 USA
来源
PROCEEDINGS OF THE 55TH ANNUAL ACM SYMPOSIUM ON THEORY OF COMPUTING, STOC 2023 | 2023年
关键词
Differential Privacy; Interactive Mechanisms; Concurrent Composition; NOISE;
D O I
10.1145/3564246.3585241
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
We study the concurrent composition properties of interactive differentially private mechanisms, whereby an adversary can arbitrarily interleave its queries to the different mechanisms. We prove that all composition theorems for non-interactive differentially private mechanisms extend to the concurrent composition of interactive differentially private mechanisms, whenever differential privacy is measured using the hypothesis testing framework of f-DP, which captures standard (epsilon, delta)-DP as a special case. We prove the concurrent composition theorem by showing that every interactive f-DP mechanism can be simulated by interactive post-processing of a non-interactive f-DP mechanism. In concurrent and independent work, Lyu (NeurIPS '22) proves a similar result to ours for (epsilon,delta)-DP, as well as a concurrent composition theorem for Renyi DP. We also provide a simple proof of Lyu's concurrent composition theorem for Renyi DP. Lyu leaves the general case of f-DP as an open problem, which we solve in this paper.
引用
收藏
页码:507 / 519
页数:13
相关论文
共 21 条
[1]  
[Anonymous], 1961, Proceedings of the fourth Berkeley Symposium on mathematical statistics and probability, DOI DOI 10.1021/JP106846B
[2]   EQUIVALENT COMPARISONS OF EXPERIMENTS [J].
BLACKWELL, D .
ANNALS OF MATHEMATICAL STATISTICS, 1953, 24 (02) :265-272
[3]   Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds [J].
Bun, Mark ;
Steinke, Thomas .
THEORY OF CRYPTOGRAPHY, TCC 2016-B, PT I, 2016, 9985 :635-658
[4]  
Dong JS, 2019, Arxiv, DOI arXiv:1905.02383
[5]  
Dwork C, 2016, Arxiv, DOI arXiv:1603.01887
[6]  
Dwork C, 2006, LECT NOTES COMPUT SC, V4004, P486
[7]   Calibrating noise to sensitivity in private data analysis [J].
Dwork, Cynthia ;
McSherry, Frank ;
Nissim, Kobbi ;
Smith, Adam .
THEORY OF CRYPTOGRAPHY, PROCEEDINGS, 2006, 3876 :265-284
[8]   The Algorithmic Foundations of Differential Privacy [J].
Dwork, Cynthia ;
Roth, Aaron .
FOUNDATIONS AND TRENDS IN THEORETICAL COMPUTER SCIENCE, 2013, 9 (3-4) :211-406
[9]  
Dwork C, 2010, ACM S THEORY COMPUT, P715
[10]   Boosting and Differential Privacy [J].
Dwork, Cynthia ;
Rothblum, Guy N. ;
Vadhan, Salil .
2010 IEEE 51ST ANNUAL SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE, 2010, :51-60