Data sketching has emerged as a key infrastructure for large-scale data analysis on streaming and distributed data. Merging sketches enables efficient estimation of cardinalities and frequency histograms over distributed data. However, merging sketches can require that each sketch stores hash codes for identifiers in different data sets or partitions, in order to perform effective matching. This can reveal identifiers during merging or across different data set or partition owners. This paper presents a framework to use noisy hash codes, with the noise level selected to obfuscate identifiers while allowing matching, with high probability. We give probabilistic error bounds on simultaneous obfuscation and matching, concluding that this is a viable approach.

翻译：数据概要化已成为流式与分布式数据大规模分析的关键基础架构。合并概要图可实现对分布式数据基数和频率直方图的高效估计。然而，为进行有效匹配，合并概要图通常需要每个概要图存储不同数据集或分区中标识符的哈希码，这可能导致合并过程中或跨不同数据集/分区所有者泄露标识符。本文提出一种使用含噪哈希码的框架，通过选择适当的噪声水平，在允许高概率匹配的同时实现标识符混淆。我们给出了同步混淆与匹配的概率误差界，证实了该方法的可行性。