The Bloom filter is a simple yet space-efficient probabilistic data structure that supports membership queries for dramatically large datasets. It is widely utilized and implemented across various industrial scenarios, often handling massive datasets that include sensitive user information necessitating privacy preservation. To address the challenge of maintaining privacy within the Bloom filter, we have developed the DPBloomfilter. This innovation integrates the classical differential privacy mechanism, specifically the Random Response technique, into the Bloom filter, offering robust privacy guarantees under the same running complexity as the standard Bloom filter. Through rigorous simulation experiments, we have demonstrated that our DPBloomfilter algorithm maintains high utility while ensuring privacy protections. To the best of our knowledge, this is the first work to provide differential privacy guarantees for the Bloom filter for membership query problems.

翻译：布隆过滤器是一种简洁且空间高效的随机数据结构，能够支持对超大规模数据集进行成员查询。该结构已在多种工业场景中得到广泛应用与实现，通常处理包含敏感用户信息且需隐私保护的海量数据集。为解决布隆过滤器中的隐私保护难题，我们提出了DPBloomfilter。该创新将经典差分隐私机制（特别是随机响应技术）集成至布隆过滤器，在保持与标准布隆过滤器相同时间复杂度的同时提供强隐私保障。通过严格的模拟实验，我们证明DPBloomfilter算法在确保隐私保护的同时仍保持较高的实用性。据我们所知，这是首个为布隆过滤器成员查询问题提供差分隐私保障的研究工作。