VD-PSI: Verifiable Delegated Private Set Intersection on Outsourced Private Datasets

Private set intersection (PSI) protocols have many real world applications. With the emergence of cloud computing the need arises to carry out PSI on outsourced datasets where the computation is delegated to the cloud. However, due to the possibility of cloud misbehavior, it is essential to verify t...

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Bibliographic Details
Published inFinancial Cryptography and Data Security Vol. 9603; pp. 149 - 168
Main Authors Abadi, Aydin, Terzis, Sotirios, Dong, Changyu
Format Book Chapter
LanguageEnglish
Published Germany Springer Berlin / Heidelberg 2017
Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
Online AccessGet full text

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Summary:Private set intersection (PSI) protocols have many real world applications. With the emergence of cloud computing the need arises to carry out PSI on outsourced datasets where the computation is delegated to the cloud. However, due to the possibility of cloud misbehavior, it is essential to verify the integrity of any outsourced datasets, and results of any delegated computation. Verifiable Computation on private datasets that does not leak any information about the data is very challenging, especially when the datasets are outsourced independently by different clients. In this paper we present VD-PSI, a protocol that allows multiple clients to outsource their private datasets and delegate computation of set intersection to the cloud, while being able to verify the correctness of the result. Clients can independently prepare and upload their datasets, and with their agreement can verifiably delegate the computation of set intersection an unlimited number of times, without the need to download or maintain a local copy of their data. The protocol ensures that the cloud learns nothing about the datasets and the intersection. VD-PSI is efficient as its verification cost is linear to the intersection cardinality, and its computation and communication costs are linear to the (upper bound of) dataset cardinality. Also, we provide a formal security analysis in the standard model.
ISBN:3662549697
9783662549698
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-662-54970-4_9