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:双方差分隐私允许不信任彼此的两个方参与数据的共同分析,同时保持个人级别的隐私。我们证明,任何通过黑盒访问密钥协议(仅此而已)实现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,仅仅存在有效的密钥协议不足以实现高效的计算差分隐私协议。通过这样做,我们在回答Vadhan对计算差分隐私所需的最小计算假设的一个开放性问题上取得了进展。结合McGregor、Mironov、Pitassi、Reingold、Talwar和Vadhan在[FOCS'10]中的信息理论下界,我们展示了不存在完全的从计算汉明距离(或等价于整数的内积)的高效计算差分隐私协议的黑盒约简,误差低于$O\left(\frac{\sqrt{n}}{e^{\epsilon}\log(n)}\right)$,到密钥协议的情况。这与Haitner、Mazor、Silbak和Tsfadia在[STOC'22]中所示的计算汉明距离蕴含着密钥协议的结果相呼应。我们得出结论,对于计算内积,密钥协议比计算差分隐私更加弱,从而回答了他们的开放性问题,即密钥协议是否足够。