![]() 2196–2207 (2004)Ĭoncas, F., Xu, P., Hoque, M.A., Lu, J., Tarkoma, S.: Multiple set matching with bloom matrix and bloom vector. IEEE (2017)Ĭhang, F., Wu-chang Feng, K.L.: Approximate caches for packet classification. In: 2017 19th Asia-Pacific network operations and management symposium (APNOMS), pp. 68, 4–16 (2015)Ĭhang, D.C., Chen, C., Thanavel, M.: Dynamic reordering bloom filter. ![]() Springer, Berlin Heidelberg, Berlin, Heidelberg (2006)Ĭalderoni, L., Palmieri, P., Maio, D.: Location privacy without mutual trust: the spatial bloom filter. 1–31 (2002)īonomi, F., Mitzenmacher, M., Panigrahy, R., Singh, S., Varghese, G.: An improved construction for counting bloom filters. For example, scalable private set intersection based on OT extension by Pinkas 18 and the private set intersection on outsourced private data sets by Aydin 27 have high efficiency with less data, but when computing the intersection of \(2^|\).Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. In literature 26, the performance of the current proposed PSI cardinality protocols are compared. Such criticism, however, is not without foundation. The other reason is that the advent of quantum computing, the increasing power of algorithms poses a great challenge to the security of classical cryptography which is based on unconfirmed arduous hypothesis 16. It’s hard to improve performance just by scaling up the hardware. One reason is that the efficiency and performance becomes outrageous when the input size becomes larger and larger. And these protocols are often viewed as inconsistent with reality. In these proposed protocols, most of them are based on classical cryptography. In recent years more and more PSI cardinality protocols are proposed, e.g. Such as, PSI cardinality has been used in privacy preserving data mining 3, information-sharing 4, human genome research 5, national security 6, Botnet identification 7, medical data preserving 8, 9, social networks 10, 11, location privacy protecting 12, 13, searchable encryption scheme 14 and anonymous authentication 15, 16. The main reason of PSI cardinality has been widely studied is that it has many real applications. And then the clients get the intersection cardinality and the server get nothing after processing the protocol. PSI cardinality enables multi-parties, one server and some clients, to jointly calculate the intersection cardinality with their private sets. One of the aspects of privacy protection research is about the Private Set Intersection (PSI) 1, 2 cardinality. In the era of “big data”, efficiency has become a key standard in designing privacy protection protocols. So privacy-protecting in large scale data processing brings new challenges to us: how to protect the data privacy with the large scale data processing, and how to meet the quick-speed and throughput rate of modern applications. Such as, geneticists should search several billion base pairs in an individual’s genome to study genetic diseases, epidemiologists need to access to medical databases which contain records of thousands of millions patients to study risk factors for disease, Online retailers hope to increase customer satisfaction by linking their transaction records to their customers’ social networking activities. Meanwhile, the data scale for processing and protecting is getting larger and larger. For this reason, there are many security solutions to protect privacy when data is processing or transmitting. But it is still a challenging task for protecting data privacy in using and transmission. ![]() For example, US privacy law COPPA, England Data Protection Act, Swedish Data Act, and other various of national privacy regulations. Protecting data privacy is a very important technology, which is a legal obligation in many countries. 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. The protocol implements privacy protection without increasing the computational complexity and communication complexity, which are independent with data scale. ![]() The validity of the protocol is verified by comparing with other protocols. Thus it is more likely to realize through the present technologies. The protocol uses single photons, so it only need to do some simple single-photon operations and tests. It is a completely novel constructive protocol for computing the intersection cardinality by using Bloom filter. In this paper, we propose a novel quantum private set intersection cardinality based on Bloom filter, which can resist the quantum attack. It is equivalent to a secure, distributed database query and has many practical applications in privacy preserving and data sharing. Private Set Intersection Cardinality that enable Multi-party to privately compute the cardinality of the set intersection without disclosing their own information.
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