Multi-Party Private Set Intersection Protocols for Practical Applications

2021 ◽  
Author(s):  
Asli Bay ◽  
Zeki Erkin ◽  
Mina Alishahi ◽  
Jelle Vos
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bai Liu ◽  
Ou Ruan ◽  
Runhua Shi ◽  
Mingwu Zhang

AbstractPrivate Set Intersection Cardinality that enable Multi-party to privately compute the cardinality of the set intersection without disclosing their own information. It is equivalent to a secure, distributed database query and has many practical applications in privacy preserving and data sharing. In this paper, we propose a novel quantum private set intersection cardinality based on Bloom filter, which can resist the quantum attack. It is a completely novel constructive protocol for computing the intersection cardinality by using Bloom filter. The protocol uses single photons, so it only need to do some simple single-photon operations and tests. Thus it is more likely to realize through the present technologies. The validity of the protocol is verified by comparing with other protocols. The protocol implements privacy protection without increasing the computational complexity and communication complexity, which are independent with data scale. Therefore, the protocol has a good prospects in dealing with big data, privacy-protection and information-sharing, such as the patient contact for COVID-19.


Author(s):  
Yalian Qian ◽  
Jian Shen ◽  
Pandi Vijayakumar ◽  
Pradip Kumar Sharma

2019 ◽  
Vol 2019 (3) ◽  
pp. 6-25 ◽  
Author(s):  
Adam Groce ◽  
Peter Rindal ◽  
Mike Rosulek

Abstract In this work we demonstrate that allowing differentially private leakage can significantly improve the concrete performance of secure 2-party computation (2PC) protocols. Specifically, we focus on the private set intersection (PSI) protocol of Rindal and Rosulek (CCS 2017), which is the fastest PSI protocol with security against malicious participants. We show that if differentially private leakage is allowed, the cost of the protocol can be reduced by up to 63%, depending on the desired level of differential privacy. On the technical side, we introduce a security model for differentially-private leakage in malicious-secure 2PC. We also introduce two new and improved mechanisms for “differentially private histogram overestimates,” the main technical challenge for differentially-private PSI.


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