Local differential privacy has been considered the standard measurement for privacy preservation in distributed data collection. Corresponding mechanisms have been designed for multiple types of tasks, like the frequency estimation for categorical values and the mean value estimation for numerical values. However, the histogram publication of numerical values, containing abundant and crucial clues for the whole dataset, has not been thoroughly considered under this measurement. To simply encode data into different intervals upon each query will soon exhaust the bandwidth and the privacy budgets, which is infeasible for real scenarios. Therefore, this paper proposes a highly efficient framework for differentially private histogram publication of numerical values in a distributed environment. The proposed algorithms can efficiently adopt the correlations among multiple queries and achieve an optimal resource consumption. We also conduct extensive experiments on real-world data traces, and the results validate the improvement of proposed algorithms.

中文翻译：

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局部差分隐私下数值的直方图发布

本地差异隐私已被视为分布式数据收集中隐私保护的标准度量。已经针对多种类型的任务设计了相应的机制，例如用于分类值的频率估计和用于数值的平均值估计。但是，在此度量范围内，尚未彻底考虑数值的直方图发布，其中包含整个数据集的丰富且关键的线索。在每次查询时简单地将数据编码为不同的间隔将很快耗尽带宽和隐私预算，这在实际情况下是不可行的。因此，本文提出了一种高效的框架，用于分布式环境中数值的差分私有直方图发布。所提出的算法可以有效地利用多个查询之间的相关性，并实现最佳的资源消耗。我们还对现实世界的数据轨迹进行了广泛的实验，结果验证了所提出算法的改进。