Cloud-Assisted Private Set Intersection via Multi-Key Fully Homomorphic Encryption

Abstract

With the development of cloud computing and big data, secure multi-party computation, which can collaborate with multiple parties to deal with a large number of transactions, plays an important role in protecting privacy. Private set intersection (PSI), a form of multi-party secure computation, is a formidable cryptographic technique that allows the sender and the receiver to calculate their intersection and not reveal any more information. As the data volume increases and more application scenarios emerge, PSI with multiple participants is increasingly needed. Homomorphic encryption is an encryption algorithm designed to perform a mathematical-style operation on encrypted data, where the decryption result of the operation is the same as the result calculated using unencrypted data. In this paper, we present a cloud-assisted multi-key PSI (CMPSI) system that uses fully homomorphic encryption over the torus (TFHE) encryption scheme to encrypt the data of the participants and that uses a cloud server to assist the computation. Specifically, we design some TFHE-based secure computation protocols and build a single cloud server-based private set intersection system that can support multiple users. Moreover, security analysis and performance evaluation show that our system is feasible. The scheme has a smaller communication overhead compared to existing schemes.

A Practical Multiparty Private Set Intersection Protocol Based on Bloom Filters for Unbalanced Scenarios

“…We set the amount of data for the client and server to 2 8 and increased the number of participants from 2 4 to 2 9 for these experiments, which aimed at testing the runtime of individual clients and the server. The experimental results are shown in Table 3 and Figure 2.…”

“…The server and client dataset size was fixed at 2 8 . The number of participants gradually increased from 2 4 to 2 9 .…”

“…Private Set Intersection (PSI) addresses the current need for a solution to compute the intersection of two or more datasets without disclosing information about the data of each party, except for the intersection itself. PSI has wide applications such as social discovery [1][2][3], document-like detection [4], joint learning in neural network models [5], suspect detection [6], privacy-preserving data mining [7], privacy-preserving retrieval systems [8], cloud-based applications [9], and so on. Many high-performance PSI protocols have been proposed recently [10][11][12][13][14][15][16].…”

Blockchain-Based Unbalanced PSI with Public Verification and Financial Security

“…With the advent of cloud computing, PSI protocols relying on a third party have been introduced in the literature [26,27]. In these protocols, participants encode their private data through a random function and send the encoded values to a third party, who computes the intersection and returns it to each party.…”

Efficient multiparty private set intersection protocol based on function secret sharing

“…MPSI denotes the privacy set intersection protocol involving a multitude of participating entities, exceeding the count of two. Presently, MPSI protocols are predominantly realized through techniques such as oblivious transfer [5] , homomorphic encryption [6] , oblivious polynomial computation [7] and quantum cryptography [8] . While these techniques ensure privacy guarantees within a public-key cryptography framework, they entail substantial computational overhead.…”