共 50 条
Amplification by Shuffling without Shuffling
被引:0
|作者:
Balle, Borja
[1
]
Bell, James
[2
]
Gascon, Adria
[3
]
机构:
[1] Google DeepMind, London, England
[2] Google, London, England
[3] Google, Mountain View, CA 94043 USA
来源:
关键词:
Differential Privacy;
Secure Computation;
Shuffle Model;
D O I:
10.1145/3576915.3623215
中图分类号:
TP18 [人工智能理论];
学科分类号:
081104 ;
0812 ;
0835 ;
1405 ;
摘要:
Motivated by recent developments in the shuffle model of differential privacy, we propose a new approximate shuffling functionality called Alternating Shuffle, and provide a protocol implementing alternating shuffling in a single-server threat model where the adversary observes all communication. Unlike previous shuffling protocols in this threat model, the per-client communication of our protocol only grows sub-linearly in the number of clients. Moreover, we study the concrete efficiency of our protocol and show it can improve per-client communication by one or more orders of magnitude with respect to previous (approximate) shuffling protocols. We also show a differential privacy amplification result for alternating shuffling analogous to the one for uniform shuffling, and demonstrate that shuffling-based protocols for secure summation based a construction of Ishai et al. [36] remain secure under the Alternating Shuffle. In the process we also develop a protocol for exact shuffling in single-server threat model with amortized logarithmic communication per-client which might be of independent interest.
引用
收藏
页码:2292 / 2305
页数:14
相关论文