Recently, we proposed a quantum solution to the problem of private set intersection cardinality (PSI-CA) (Information Sciences 370–371 (2016) 147–158). Compared to classical solutions, the proposed quantum PSI-CA protocol achieves an exponential reduction in communication complexity, since it only needs
O(1)communication cost. However, this protocol requires two additional assumptions about the cardinalities of the sets, which may limit its wider applications. In this paper, we successfully discard these assumptions and present a stronger quantum PSI-CA protocol without any limitation. The new protocol ensures the parties’ private security, i.e., unconditionally secure server privacy and statistically secure client privacy, and it achieves the constant computation and communication complexities, which are independent of the size of the sets. Therefore, it is more suitable for practical applications with big data sets.
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.
ABSTRACT Disease surveillance is vital for public health security, allowing early outbreak detection, reducing transmission, safeguarding population health, and aiding emergency response. This paper proposes a quantum disease surveillance algorithm based on private set intersection (PSI). Specifically, we use the BHT algorithm to solve the PSI problem and improve computational efficiency through candidate subset preprocessing and quantum parallel search mechanisms. It achieves a polynomial speedup over previous algorithms by reducing the communication complexity from to and optimizing the round complexity to a constant, thereby substantially reducing the data transmission overhead. Simulation experiments on the IBM quantum platform verify the correctness and feasibility of our algorithm. Security analysis shows that our algorithm effectively resists both insider and outsider attacks while satisfying privacy‐preservation requirements in disease surveillance scenarios, specifically protecting the privacy of individual clients and the server.