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Quantum private set intersection cardinality based on bloom filter.

Bai Liu1, Ou Ruan2, Runhua Shi2

  • 1School of Computer Science, Hubei University of Technology, Wuhan, 430068, China. liubai@hbut.edu.cn.

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|August 31, 2021
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Summary
This summary is machine-generated.

We introduce a new quantum-resistant method for private set intersection cardinality using Bloom filters. This approach enables secure data sharing and privacy protection, even against quantum attacks.

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Area of Science:

  • Quantum Information Science
  • Cryptography
  • Computer Science

Background:

  • Private Set Intersection Cardinality (PSIC) is crucial for secure, multi-party data analysis without revealing sensitive information.
  • Existing PSIC protocols face vulnerabilities, particularly against emerging quantum computing threats.
  • Applications span secure data sharing, distributed database queries, and contact tracing.

Purpose of the Study:

  • To propose a novel quantum-resistant protocol for Private Set Intersection Cardinality (PSIC).
  • To leverage Bloom filters for efficient and secure intersection cardinality computation.
  • To ensure privacy protection and resist quantum attacks.

Main Methods:

  • Developed a constructive protocol for PSIC utilizing Bloom filters.
  • Employed single-photon operations and tests for protocol implementation.
  • Validated the protocol's effectiveness through comparative analysis with existing methods.

Main Results:

  • The proposed protocol offers quantum resistance for PSIC.
  • It achieves privacy protection without escalating computational or communication complexity.
  • Performance is independent of data scale, making it suitable for big data scenarios.

Conclusions:

  • The novel Bloom filter-based quantum PSIC protocol is a significant advancement in privacy-preserving technologies.
  • Its reliance on simple single-photon operations enhances feasibility with current technology.
  • The protocol shows strong potential for applications like COVID-19 contact tracing and large-scale secure data sharing.