Efficient and Quasi-accurate Multiparty Private Set Union

By Katsunari Shishido, Atsuko Miyaji. 2018

17 citations recorded by the source corpus.

Both scalability and flexibility have become crucial for privacy-preserving protocols in the age of big data. Multiparty private set union (MPSU) is an important privacy-preserving protocol. Previous MPSU protocols for multisets based on a polynomial are not efficient, although they output a precise set union. However, the previous MPSU implementation based on a Bloom filter is efficient but only roughly estimates the number of duplicates of the set union. In this paper, we propose a new duplicated Bloom filter to increase the chances of computing the number of duplicates correctly. Using this filter, we propose a scalable and flexible MPSU for multisets.

Citations in the corpus

• Secure Federated Submodel Learning (2019, neutral): “In the cryptography field, according to the representation format of a set, the protocols can be generally divided into two categories: polynomial based [88]–[92] and Bloom filter based [93]–[95].”