Private set intersection: A systematic literature review

Morales, Daniel; Agudo, Isaac; López, Javier

doi:10.1016/j.cosrev.2023.100567

Cited by 13 publications

(3 citation statements)

References 59 publications

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“…It can be seen that the Cuckoo filter significantly reduces the storage on the client, especially when the size of the server's set is large. 20 9.88 3 2 24 174 48 2 28 3040 768…”

Section: Methodsmentioning

confidence: 99%

“…Freedman et al [23] first formally defined PSI and introduced a PSI protocol based on oblivious polynomial evaluation. Since then, many efficient PSI protocols have been proposed, among which protocols based on OT extension present the most competitive performance [24]. Pinkas et al [5] proposed the first OT-based PSI protocol under the semihonest model, followed by a series of improvements in works [6][7][8][9].…”

Section: Related Workmentioning

confidence: 99%

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Blockchain-Based Unbalanced PSI with Public Verification and Financial Security

Wang,

2024

Mathematics

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Private set intersection (PSI) enables two parties to determine the intersection of their respective datasets without revealing any information beyond the intersection itself. This paper particularly focuses on the scenario of unbalanced PSI, where the sizes of datasets possessed by the parties can significantly differ. Current protocols for unbalanced PSI under the malicious security model exhibit low efficiency, rendering them impractical in real-world applications. By contrast, most efficient unbalanced PSI protocols fail to guarantee the correctness of the intersection against a malicious server and cannot even ensure the client’s privacy. The present study proposes a blockchain-based unbalanced PSI protocol with public verification and financial security that enables the client to detect malicious behavior from the server (if any) and then generate an irrefutable and publicly verifiable proof without compromising its secret. The proof can be verified through smart contracts, and some economic incentive and penalty measures are executed automatically to achieve financial security. Furthermore, we implement the proposed protocol, and experimental results demonstrate that our scheme exhibits low online communication complexity and computational overhead for the client. At the same time, the size of the generated proof and its verification complexity are both O(logn), enabling cost-effective validation on the blockchain.

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“…It can be seen that the Cuckoo filter significantly reduces the storage on the client, especially when the size of the server's set is large. 20 9.88 3 2 24 174 48 2 28 3040 768…”

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Blockchain-Based Unbalanced PSI with Public Verification and Financial Security

Wang,

2024

Mathematics

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“…For instance, if the sizes of the private sets are similar, it is refer to as a balanced PSI while otherwise it is called an unbalanced one. More generally, a classification has recently been proposed in [23].…”

Section: Private Set Intersectionmentioning

confidence: 99%

Probonite

Azogagh

Delfour

Gambs

et al. 2022

Proceedings of the 10th Workshop on Encrypted Computing &Amp; Applied Homomorphic Cryptography

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Graphs are a widely used data structure for collecting and analyzing relational data. However, when the graph structure is distributed across several parties, its analysis is particularly challenging. In particular, due to the sensitivity of the data each party might want to keep their partial knowledge of the graph private, while still willing to collaborate with the other parties for tasks of mutual benefit, such as data curation or the removal of poisoned data. To address this challenge, we propose Crypto'Graph, an efficient protocol for privacy-preserving link prediction on distributed graphs. More precisely, it allows parties partially sharing a graph with distributed links to infer the likelihood of formation of new links in the future. Through the use of cryptographic primitives, Crypto'Graph is able to compute the likelihood of these new links on the joint network without revealing the structure of the private individual graph of each party, even though they know the number of nodes they have, since they share the same graph but not the same links. Crypto'Graph improves on previous works by enabling the computation of a certain number of similarity metrics without any additional cost. The use of Crypto'Graph is illustrated for defense against graph poisoning attacks, in which it is possible to identify potential adversarial links without compromising the privacy of the graphs of individual parties. The effectiveness of Crypto'Graph in mitigating graph poisoning attacks and achieving high prediction accuracy on a graph neural network node classification task is demonstrated through extensive experimentation on a real-world dataset.

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