Paper 2018/496

Efficient Delegated Private Set Intersection on Outsourced Private Datasets

Aydin Abadi, Sotirios Terzis, Roberto Metere, and Changyu Dong


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.

Available format(s)
Cryptographic protocols
Publication info
Published elsewhere. IEEE Transactions on Dependable and Secure Computing
Private Set IntersectionSecure ComputationCloud Computing
Contact author(s)
changyu dong @ gmail com
2018-05-23: received
Short URL
Creative Commons Attribution


      author = {Aydin Abadi and Sotirios Terzis and Roberto Metere and Changyu Dong},
      title = {Efficient Delegated Private Set Intersection on Outsourced Private Datasets},
      howpublished = {Cryptology ePrint Archive, Paper 2018/496},
      year = {2018},
      doi = {10.1109/TDSC.2017.2708710},
      note = {\url{}},
      url = {}
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