Efficient Noise Generation Protocols for Differentially Private Multiparty Computation

Reo Eriguchi, Atsunori Ichikawa, N. Kunihiro, K. Nuida
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Abstract

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 $(\epsilon,\delta )$(ε,δ)-differential privacy, they only need constant numbers of rounds independent of $\epsilon,\delta$ε,δ while the previous protocol needs the number of rounds depending on $\delta$δ. 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 of $\epsilon$ε or $\delta$δ for the computational analogue of $(\epsilon,\delta )$(ε,δ)-differential privacy while the previous protocols require communication complexity depending on $\epsilon$ε. We also prove that our protocols can be extended so that they provide differential privacy in the active setting.
差分私有多方计算的高效噪声生成协议
为了解决协议输出中存在的信息泄漏问题,构建受噪声干扰的安全的多方计算协议是非常重要的。然而,以前的噪声生成协议的循环和通信复杂性随着隐私预算的差异而增长,或者要求各方在局部产生非均匀噪声,这使得难以保证对主动对手的差异隐私。我们提出了三种协议,用于从提供差分隐私的特定分布中生成噪声。这两种方法从离散拉普拉斯分布的有限范围变异体中产生噪声。对于(λ,δ) -差分隐私,它们只需要独立于λ的常数轮数,而之前的协议需要依赖于δ的轮数。这两个协议是无可比拟的,因为它们在轮询和通信复杂性之间进行了权衡。我们的第三个协议通过预分配伪随机函数的密钥,非交互地从二项分布中生成噪声份额。对于(λ,δ)差分隐私的计算模拟,它实现了独立于λ或δ的通信复杂度,而以前的协议需要依赖于λ的通信复杂度。我们还证明了我们的协议可以扩展,以便在活动设置中提供不同的隐私。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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