We present the first $\varepsilon$-differentially private, computationally efficient algorithm that estimates the means of product distributions over $\{0,1\}^d$ accurately in total-variation distance, whilst attaining the optimal sample complexity to within polylogarithmic factors. The prior work had either solved this problem efficiently and optimally under weaker notions of privacy, or had solved it optimally while having exponential running times.

翻译：我们提出了首个$\varepsilon$-差分隐私且计算高效的算法，该算法能够在全变差距离下精确估计$\{0,1\}^d$上乘积分布的均值，同时以多项式对数因子范围内的最优样本复杂度实现这一目标。此前的研究要么在较弱隐私概念下高效且最优地解决了该问题，要么在指数时间复杂度下实现了最优求解。