Scalable Multi-party Private Set-Intersection

Hazay, Carmit; Venkitasubramaniam, Muthuramakrishnan

doi:10.1007/978-3-662-54365-8_8

Cited by 81 publications

(52 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their construction attains O(n•v max ) and O (n • v max ) as communication and computation overhead complexities for the designated party. Hazay and Venkitasubramaniam [39] proposed an MPSI protocol utilizing the two-party PSI protocol of Freedman et al [32], and very recently, Kolesnikov et al [47] presented a new paradigm for MPSI in a semi-honest setting from symmetric key techniques.…”

Section: Related Workmentioning

confidence: 99%

“…• We next extend our MPSI-CA to an MPSI protocol without changing the security attributes. Similar to [39], we use a star network topology instead of point-to-point fully connected network. In this setting, a single designated party, communicates individually with every other party via a variant of the two-party PSI of [13].…”

Section: Related Workmentioning

confidence: 99%

“…Computation cost of each participant is O(v max k) except the designated party, for which the cost is O(v 1 ). Unlike the existing protocols [47,39,49,51,52,10,12,46], individual computation complexity of each participant does not depend on the number of participants n in our scheme. Similar to [49], our scheme is flexible as each party's input set size is independent from the others.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Secure and efficient multiparty private set intersection cardinality

Debnath¹,

Stănică²,

Kundu³

et al. 2021

Advances in Mathematics of Communications

View full text Add to dashboard Cite

In the field of privacy preserving protocols, Private Set Intersection (PSI) plays an important role. In most of the cases, PSI allows two parties to securely determine the intersection of their private input sets, and no other information. In this paper, employing a Bloom filter, we propose a Multiparty Private Set Intersection Cardinality (MPSI-CA), where the number of participants in PSI is not limited to two. The security of our scheme is achieved in the standard model under the Decisional Diffie-Hellman (DDH) assumption against semi-honest adversaries. Our scheme is flexible in the sense that set size of one participant is independent from that of the others. We consider the number of modular exponentiations in order to determine computational complexity. In our construction, communication and computation overheads of each participant is O(vmaxk) except that the complexity of the designated party is O(v 1), where vmax is the maximum set size, v 1 denotes the set size of the designated party and k is a security parameter. Particularly, our MSPI-CA is the first that incurs linear complexity in terms of set size, namely O(nvmaxk), where n is the number of participants. Further, we extend our MPSI-CA to MPSI retaining all the security attributes and other properties. As far as we are aware of, there is no other MPSI so far where individual computational cost of each participant is independent of the number of participants. Unlike MPSI-CA, our MPSI does not require any kind of broadcast channel as it uses star network topology in the sense that a designated party communicates with everyone else.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Secure and efficient multiparty private set intersection cardinality

Debnath¹,

Stănică²,

Kundu³

et al. 2021

Advances in Mathematics of Communications

View full text Add to dashboard Cite

show abstract

“…The former has a linear communication cost, but, incurs O(k v .k c ) computations. In the latter, authors rely on expensive generic zero knowledge proofs to achieve correctness [5,10]. Later, Dachman-Soled et al [21] proposed a PSI protocol based on [20].…”

Section: Related Workmentioning

confidence: 99%

“…Recently, there has been a great interest in improving the efficiency of maliciousresistant PSI protocols [9,6,10]. Several works [11,5] have achieved linear communication and computation complexities under non-standard security models as the Oracle [12] and the Common Reference String [13] models.…”

Section: Introductionmentioning

confidence: 99%

Private and Efficient Set Intersection Protocol for Big Data Analytics

Gheid

Challal

2017

Algorithms and Architectures for Parallel Processing

View full text Add to dashboard Cite

Private Set Intersection (PSI) is a fundamental multi-party computation primitive used to secure many political, commercial, and social applications. PSI allows mistrustful parties to compute the intersection of their private sets without leaking additional information. PSI protocols has been largely proposed for both the semi-honest and the malicious settings. Nevertheless, the semi-honest setting does not suffice in many realistic scenarios and security in the malicious setting is built upon cryptographic schemes, which require hard assumptions and induce a high computational cost. In this work, we propose a novel twoparty PSI protocol secure under the mixed model, where the server may be semi-honest and the client may be malicious. We build our protocol upon matrix algebra without using any cryptographic schemes or nonstandard assumptions and we provide simulation-based security proof. Our protocol achieves a linear asymptotic complexity O(kv + kc) for communications and server computations, where kv and kc are sizes of participating parties' sets. Besides, we compare empirical performance of our solution to the insecure hashing solution used in practice. Experimental results reveal efficiency and scalability of our new PSI protocol, which makes it adequate for Big Data analytics.

show abstract

Server‐aided multiparty private set intersection protocols for lightweight clients and the application in intelligent logistics

Niu

Wang

2022

Int J of Intelligent Sys

View full text Add to dashboard Cite

In numerous data application scenarios, various data can be represented in the form of data sets, and the intersection is often the common concern of multiple users. Using private set intersection (PSI) protocol, users can securely compute the intersection of their sets without disclosing their private input and other additional information. At the same time, there is also a strong practical demand for statistical analysis of intersection data. However, when multiple parties are involved, the efficiency of the multiparty PSI protocol decreases dramatically as the number of users increases. In this paper, we construct a novel server‐aided multiparty PSI protocol, which can transform the complex multiparty computation problem into an efficient two‐party computation problem. In addition, we design a series of server‐aided party PSI statistical protocols to compute the statistics of the intersection elements, such as the sum, average, variance, range (maximum, minimum), and the cardinality of intersection (the size of intersection). In our protocol, the clients only need to upload their private data to the servers in blinded form and do not need to keep online during server computing. Experiments show that our protocol has high computation and communication efficiency and is suitable for lightweight clients. In addition, we also introduce an application of our protocols in the field of intelligent logistics.

show abstract

Scalable Multi-party Private Set-Intersection

Cited by 81 publications

References 59 publications

Secure and efficient multiparty private set intersection cardinality

Secure and efficient multiparty private set intersection cardinality

Private and Efficient Set Intersection Protocol for Big Data Analytics

Server‐aided multiparty private set intersection protocols for lightweight clients and the application in intelligent logistics

Contact Info

Product

Resources

About