Two party differential privacy allows two parties who do not trust each other, to come together and perform a joint analysis on their data whilst maintaining individual-level privacy. We show that any efficient, computationally differentially private protocol that has black-box access to key agreement (and nothing stronger), is also an efficient, information-theoretically differentially private protocol. In other words, the existence of efficient key agreement protocols is insufficient for efficient, computationally differentially private protocols. In doing so, we make progress in answering an open question posed by Vadhan about the minimal computational assumption needed for computational differential privacy. Combined with the information-theoretic lower bound due to McGregor, Mironov, Pitassi, Reingold, Talwar, and Vadhan in [FOCS'10], we show that there is no fully black-box reduction from efficient, computationally differentially private protocols for computing the Hamming distance (or equivalently inner product over the integers) on $n$ bits, with additive error lower than $O\left(\frac{\sqrt{n}}{e^{\epsilon}\log(n)}\right)$, to key agreement. This complements the result by Haitner, Mazor, Silbak, and Tsfadia in [STOC'22], which showed that computing the Hamming distance implies key agreement. We conclude that key agreement is \emph{strictly} weaker than computational differential privacy for computing the inner product, thereby answering their open question on whether key agreement is sufficient.
翻译:双方差分隐私允许两个不信任彼此的方进行数据的联合分析,同时保持个体隐私。我们证明,任何具有黑盒密钥协商(仅此而已)访问的高效计算差分隐私协议,也是高效的信息理论差分隐私协议。换句话说,高效的密钥协商协议的存在是不足以实现高效的计算差分隐私协议的。在这样做的过程中,我们在回答Vadhan提出的有关计算差分隐私所需最小计算假设的一个未解决问题中取得了进展。与McGregor等人在[FOCS'10]中提出的信息理论下界相结合,我们表明从高效的计算差分隐私协议减少计算Hamming距离(或整数上的内积)的黑盒访问,使得添加误差低于$O\left(\frac{\sqrt{n}}{e^{\epsilon}\log(n)}\right)$,是没有可能的。这与Haitner等人在[STOC'22]中的结果相对应,该结果表明计算Hamming距离意味着密钥协商。我们得出结论,对于计算内积,密钥协商是“严格”弱于计算差分隐私,从而回答了他们有关密钥协商是否足够的问题。