For a dataset of label-count pairs, an anonymized histogram is the multiset of counts. Anonymized histograms appear in various potentially sensitive contexts such as password-frequency lists, degree distribution in social networks, and estimation of symmetric properties of discrete distributions. Motivated by these applications, we propose the first differentially private mechanism to release anonymized histograms that achieves near-optimal privacy utility trade-off both in terms of number of items and the privacy parameter. Further, if the underlying histogram is given in a compact format, the proposed algorithm runs in time sub-linear in the number of items. For anonymized histograms generated from unknown discrete distributions, we show that the released histogram can be directly used for estimating symmetric properties of the underlying distribution.

中文翻译：

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差异化私有匿名直方图

对于标签计数对的数据集，匿名直方图是计数的多重集。匿名直方图出现在各种潜在敏感的上下文中，例如密码频率列表、社交网络中的度数分布以及离散分布对称属性的估计。受这些应用程序的启发，我们提出了第一个差异私有机制来发布匿名直方图，该机制在项目数量和隐私参数方面实现了接近最佳的隐私效用权衡。此外，如果底层直方图以紧凑格式给出，则所提出的算法在时间上以项目数量次线性运行。对于从未知离散分布生成的匿名直方图，