Publication Type
Journal Article
Version
acceptedVersion
Publication Date
10-2020
Abstract
Private set intersection (PSI) is a fundamental cryptographic protocol which has a wide range of applications. It enables two clients to compute the intersection of their private datasets without revealing non-matching elements. The advent of cloud computing drives the ambition to reduce computation and data management overhead by outsourcing such computations. However, since the cloud is not trustworthy, some cryptographic methods should be applied to maintain the confidentiality of datasets. But, in doing so, data owners may be excluded from access control on their outsourced datasets. Therefore, to control access rights and to interact with authorized users, they have to be online during the protocol. On the other hand, none of the existing cloud-based PSI schemes support fine-grained access control over outsourced datasets. This paper, for the first time, proposes an attribute-based private set intersection (AB-PSI) scheme providing fine-grained access control. AB-PSI allows a data owner to control intersection computations on its outsourced dataset by defining an access control policy. We also provide security definitions for an AB-PSI scheme and prove the security of our scheme in the standard model. We implement our scheme and report performance evaluation results. (C) 2020 Elsevier Inc. All rights reserved.
Keywords
Fine-grained access control, Private set intersection, Cloud computing, Attribute-based encryption
Discipline
Information Security
Research Areas
Cybersecurity
Publication
Information Sciences
Volume
536
First Page
222
Last Page
243
ISSN
0020-0255
Identifier
10.1016/j.ins.2020.05.041
Publisher
Elsevier
Citation
ALI, Mohammad; MOHAJERI Javad; SADEGHI, Mohammad-Reza; and LIU, Ximeng.
Attribute-based fine-grained access control for outscored private set intersection computation. (2020). Information Sciences. 536, 222-243.
Available at: https://ink.library.smu.edu.sg/sis_research/5303
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.ins.2020.05.041