Local differential privacy (LDP) has recently become a popular privacy-preserving data collection technique protecting users' privacy. The main problem of data stream collection under LDP is the poor utility due to multi-item collection from a very large domain. This paper proposes PrivSketch, a high-utility frequency estimation protocol taking advantage of sketches, suitable for private data stream collection. Combining the proposed background information and a decode-first collection-side workflow, PrivSketch improves the utility by reducing the errors introduced by the sketching algorithm and the privacy budget utilization when collecting multiple items. We analytically prove the superior accuracy and privacy characteristics of PrivSketch, and also evaluate them experimentally. Our evaluation, with several diverse synthetic and real datasets, demonstrates that PrivSketch is 1-3 orders of magnitude better than the competitors in terms of utility in both frequency estimation and frequent item estimation, while being up to ~100x faster.

翻译：本地差分隐私（LDP）近年来已成为一种流行的隐私保护数据收集技术，用于保护用户隐私。在LDP框架下，数据流收集的主要问题是因从极大域中采集多个数据项而导致的效用低下。本文提出PrivSketch，一种利用Sketch技术实现高效用频率估计的协议，适用于私有数据流收集。通过结合所提出的背景信息与一种"解码优先"的采集端工作流，PrivSketch在收集多项数据时，减少了Sketch算法引入的误差并优化了隐私预算的利用，从而提升了效用。我们从理论上证明了PrivSketch在精度和隐私特性上的优越性，并通过实验进行了验证。我们在多个多样化的合成与真实数据集上的评估表明，在频率估计与高频项估计中，PrivSketch的效用比现有方法高出1-3个数量级，同时速度提升高达约100倍。