JEDI: Joint and Effective Privacy Preserving Outsourced Set Intersection and Data Integration Protocols

In the era of big data with an ever-increasing amount of data, the demand for data processing is increasing, and various computing service agreements have also been created. While data are widely used, data providers worry that their privacy will be infringed, so the security of the data has also received more attention. Private set intersection (PSI) allows two data-owning parties to jointly compute the intersection of private datasets. The party cannot obtain any other information except for the intersection results. However, Bloom filter (BF) is a naive solution that can be used to potentially obtain PSI, but the verifiability of using BF is usually a concern for effectiveness. Hence, garbled Bloom filter is indeed expected to overcome this concern in two-party setting. We aim for the outsourced scenario where there are many data-owning parties in distributed setting. These parties would like to jointly obtain the set intersection in a private manner supported by an outsourced cloud, and further to obtain data integration. The main challenges are that the previous solutions cannot be directly transformed into an outsourced scenario. In this paper, the proposed joint and effective privacy preserving outsourced set intersection and data integration protocols are called
$\textsf {JEDI}$
. They are generic constructions. We start by introducing a basic outsourced PSI protocol from the cryptographic building blocks and new BF and GBF techniques. Then, we modify the basic protocol and propose the full-fledged scheme to achieve an outsourced multi-party PSI protocol. Finally, we analyze the security and execution efficiency aspects to ensure usability.