DPBloomfilter: Securing Bloom Filters with Differential Privacy

TL;DR

This paper introduces DPBloomfilter, a novel privacy-preserving Bloom filter that integrates differential privacy, specifically Random Response, maintaining efficiency and utility while protecting sensitive data in large datasets.

Contribution

The paper presents the first differential privacy guarantee for Bloom filters, combining classical privacy mechanisms with the data structure without increasing complexity.

Findings

Maintains high utility with privacy guarantees

Operates with same complexity as standard Bloom filter

First to provide differential privacy for Bloom filters

Abstract

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.