An Efficient Privacy-Preserving Data Matching Based on Bloom Filter and 0-1 Encoding
Yanjie Tang, Chunying Wu, Lijun Yan · 2024
The increasing popularity of mobile internet, cloud computing, and big data platforms has significantly brought the convenience to the use of crowdsourcing. Data matching plays a crucial role in the sharing economy, and it is particularly important to develop efficient data matching while ensuring data privacy-preserving. Our study focuses on addressing the data matching problem applicable to multiple data owners and multiple data requesters. We propose an efficient privacy preserving data matching scheme by using 0–1 encoding to safeguard data privacy, by utilizing bilinear pairings for keyword matching and Bloom Filters for data range matching. To alleviate the computational burden on data owners, computing tasks are allocated to cloud infrastructure. Additionally, Bloom Filters are employed to reduce computation time for data range queries. Then we demonstrate its feasibility and efficiency through experimental results.