Efficient Noise Generation Protocols for Differentially Private Multiparty Computation
Efficient Noise Generation Protocols for Differentially Private Multiparty Computation
复制标题
用于差分隐私多方计算的高效噪声生成协议
DOI：
10.1109/tdsc.2022.3227568
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发表时间：
2022
影响因子：
7.3
通讯作者：
Nuida Koji
中科院分区：
文献类型：
--
作者：
Eriguchi Reo;Ichikawa Atsunori;Kunihiro Noboru;Nuida Koji
为了限制协议输出中的信息泄漏，重要的是构造安全的多方计算协议，该协议输出受噪声干扰的差分私有值。然而，以前的噪声生成协议具有随着差分隐私预算而增长的圆形和通信复杂度，或者要求各方在本地生成非均匀噪声，这使得难以保证针对活跃对手的差分隐私。我们提出了三种协议，用于生成从某些分布提供差分隐私的噪声。他们两个从离散的拉普拉斯分布的有限范围变量产生噪声。对于-差分隐私协议，它们只需要与独立的常数轮数，而前一个协议需要依赖于的轮数，这两个协议在轮数和通信复杂度之间进行了权衡，具有不可比拟性。我们的第三个协议非交互式地产生的份额的噪声从二项分布的预分配密钥的伪随机函数。它实现了通信复杂度独立于差分隐私的计算模拟，而以前的协议需要通信复杂度取决于。我们还证明了我们的协议可以扩展，使他们提供差分隐私的活动设置。
To bound information leakage in outputs of protocols, it is important to construct secure multiparty computation protocols which output differentially private values perturbed by the addition of noise. However, previous noise generation protocols have round and communication complexity growing with differential privacy budgets, or require parties to locally generate non-uniform noise, which makes it difficult to guarantee differential privacy against active adversaries. We propose three kinds of protocols for generating noise drawn from certain distributions providing differential privacy. The two of them generate noise from finite-range variants of the discrete Laplace distribution. For-differential privacy, they only need constant numbers of rounds independent ofwhile the previous protocol needs the number of rounds depending on. The two protocols are incomparable as they make a trade-off between round and communication complexity. Our third protocol non-interactively generate shares of noise from the binomial distribution by predistributing keys for a pseudorandom function. It achieves communication complexity independent oforfor the computational analogue of-differential privacy while the previous protocols require communication complexity depending on. We also prove that our protocols can be extended so that they provide differential privacy in the active setting.
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DOI：
10.1109/trustcom.2016.0157
发表时间：
2016
期刊：
2016 IEEE Trustcom/BigDataSE/ISPA
影响因子：
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作者：
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DOI：
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发表时间：
2021
期刊：
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影响因子：
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DOI：
10.1145/3319535.3354256
发表时间：
2019-11
期刊：
Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security
影响因子：
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作者：
J. Champion;Abhi Shelat;Jonathan Ullman
通讯作者：
J. Champion;Abhi Shelat;Jonathan Ullman
DOI：
--
发表时间：
2017-08
期刊：
--
影响因子：
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作者：
Tianhao Wang;Jeremiah Blocki;Ninghui Li;S. Jha
通讯作者：
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影响因子：
22.7
作者：
SHAMIR, A
通讯作者：
SHAMIR, A