Encrypted Set Intersection Protocol for Outsourced Datasets

Liu, Fang; Ng, Wee Keong; Zhang, Wei; Giang, Do Hoang; Han, Shi‐Kui

doi:10.1109/ic2e.2014.18

Cited by 35 publications

(48 citation statements)

References 26 publications

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“…When PSI computation is delegated to a server who is not fully trusted, protecting the privacy of the computation input and output from the server is crucial. However, as discussed in section 2 the protocols in [17], [19], [20] do not fully preserve data privacy and leak some information to the cloud server. Protocols like the size-hiding variation of [15], those in [16], [18], O-PSI and EO-PSI offer this protection.…”

Section: Comparisonmentioning

confidence: 99%

“…On the other hand, a number of PSI protocols that let clients delegate the computation to a server have been proposed in [15], [16], [17], [18], [19], [20]. The protocols in [17] allow clients to outsource their sets to a server by hashing each element and then adding a random value to it.…”

Section: Related Workmentioning

confidence: 99%

“…The protocols in [17] allow clients to outsource their sets to a server by hashing each element and then adding a random value to it. In this protocol, each time the computation is delegated, every client needs to download an encrypted vector whose size is equal to the client's set size.…”

Section: Related Workmentioning

confidence: 99%

“…the size. In [17], the protocol includes an authorization step. However, this step is not very effective.…”

Section: Comparisonmentioning

confidence: 99%

“…[10], [11], [12], [13], [14]). Several research efforts by industry and academia have been directed towards delegated PSI protocols to realize organizational objectives [15], [16], [17], [18], [19], [20]. However, designing a PSI protocol that allows delegation of storage and computation to the cloud is not an easy task.…”

Section: O-psi Was Introduced In a Paper That Appears In The Proceedimentioning

confidence: 99%

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Efficient Delegated Private Set Intersection on Outsourced Private Datasets

Abadi

Terzis

Metere

et al. 2019

IEEE Trans. Dependable and Secure Comput.

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Abstract-Private set intersection (PSI) is an essential cryptographic protocol that has many real world applications. As cloud computing power and popularity have been swiftly growing, it is now desirable to leverage the cloud to store private datasets and delegate PSI computation to it. Although a set of efficient PSI protocols have been designed, none support outsourcing of the datasets and the computation. In this paper, we propose two protocols for delegated PSI computation on outsourced private datasets. Our protocols have a unique combination of properties that make them particularly appealing for a cloud computing setting. Our first protocol, O-PSI, satisfies these properties by using additive homomorphic encryption and point-value polynomial representation of a set. Our second protocol, EO-PSI, is mainly based on a hash table and point-value polynomial representation and it does not require public key encryption; meanwhile, it retains all the desirable properties and is much more efficient than the first one. We also provide a formal security analysis of the two protocols in the semi-honest model and we analyze their performance utilizing prototype implementations we have developed. Our performance analysis shows that EO-PSI scales well and is also more efficient than similar state-of-the-art protocols for large set sizes.

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Section: Comparisonmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…the size. In [17], the protocol includes an authorization step. However, this step is not very effective.…”

Section: Comparisonmentioning

confidence: 99%

Section: O-psi Was Introduced In a Paper That Appears In The Proceedimentioning

confidence: 99%

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Efficient Delegated Private Set Intersection on Outsourced Private Datasets

Abadi

Terzis

Metere

et al. 2019

IEEE Trans. Dependable and Secure Comput.

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Improved outsourced private set intersection protocol based on polynomial interpolation

Yang

Xue-shan

Wang

et al. 2017

Concurrency and Computation

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SummaryPrivate set intersection (PSI) protocols enable 2 parties to compute the intersection of their inputs without compromising anything about the datasets beyond the intersection. With the advent of cloud computing, outsourcing computation has been attracted wide range of attention from research community and applied widely in the industry. The cloud computing allows resources restrained devices to outsource their expensive computation to the cloud. Based on Abadi's O-PSI, we present a variant of delegated private set intersection protocol secure in the semi-honest model under RSA assumption, and we also give an efficient and secure outsourcing computation algorithm for RSA cryptosystem. Depending on this algorithm, we transform a variant of delegated private set intersection protocol into an improved outsourced one. It enables the clients only to perform simple modular multiplication for computing what they want during the execution of protocol. Besides, the variant of delegated protocol can be easily extended to multiple clients.Compared with the state of the art, our proposed protocol has great advantage in efficiency. We finally evaluate these protocols and prove their security in the semi-honest model. KEYWORDScloud computing, outsourced computation, polynomial interpolation, private set intersection INTRODUCTIONPrivate set intersection protocols are the branch of secure multi-party computation (SMPC), 1,2 which have been studied for many years. It allows 2 parties or multiple parties to compute the intersections privately for their sets without revealing any other information. As a novel cryptographic primitive, PSI not only solves the privacy-preserving problems in datasets, but also as a building block applied in the real life settings, such as privacy-preserving data mining, 3 health care research, human genome project, 4 homeland security, 5 and information flow control. 6 For instance, suppose 2 accounts holders want to know their common friends in social network whereas they both do not want to leak their privacy. It seems to be a challengeable problem. However, with PSI protocols, 2 holders can privately know the result and do not need to publish total information.With the advent of cloud computing, outsourced computation 7 has been the main tendency for clients with limited computational resources. It allows them to outsource their private data to the cloud to compute the results. Using powerful cloud computing to construct and implement private set intersection protocol is becoming a new scientific research field. On the one hand, cloud computing provides huge resources for us to achieve efficient private set intersection protocol. On the other hand, it also brings about some challenges.In traditional private set intersection protocol, it only involves 2 participants to interact with each other. However, in the cloud computing environment, the cloud server as the third party takes part in the private set intersection protocol, which is not totally trusted. Therefore, the datasets transferred to...

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

Niu

Wang

2022

Int J of Intelligent Sys

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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.

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Encrypted Set Intersection Protocol for Outsourced Datasets

Cited by 35 publications

References 26 publications

Efficient Delegated Private Set Intersection on Outsourced Private Datasets

Efficient Delegated Private Set Intersection on Outsourced Private Datasets

Improved outsourced private set intersection protocol based on polynomial interpolation

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

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