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Quantum private set intersection cardinality based on bloom filter.
Liu, Bai; Ruan, Ou; Shi, Runhua; Zhang, Mingwu.
  • Liu B; School of Computer Science, Hubei University of Technology, Wuhan, 430068, China. liubai@hbut.edu.cn.
  • Ruan O; School of Computer Science, Hubei University of Technology, Wuhan, 430068, China.
  • Shi R; School of Computer Science, Hubei University of Technology, Wuhan, 430068, China.
  • Zhang M; School of Computer Science, Hubei University of Technology, Wuhan, 430068, China. csmwzhang@gmail.com.
Sci Rep ; 11(1): 17332, 2021 08 30.
Article in English | MEDLINE | ID: covidwho-1379335
ABSTRACT
Private 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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Computer Security / Confidentiality / COVID-19 Limits: Humans Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-96770-1

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Computer Security / Confidentiality / COVID-19 Limits: Humans Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-96770-1