Publication Type

Journal Article

Publication Date

12-2017

Abstract

In a data streaming model, a data owner releases records or documents to a set of users with matching interests, in such a way that the match in interest can be calculated from the correlation between each pair of document and user query. For scalability and availability reasons, this calculation is delegated to third-party servers, which gives rise to the need to protect the integrity and privacy of the documents and user queries. In this paper, we propose a server-aided data stream monitoring scheme (DSM) to address the aforementioned integrity and privacy challenges, so that the users are able to verify the correlation scores obtained from the server. The scheme provides strong security protection, even in the event of collusion between the server and other users. We also offer techniques to bound the computation demand in decoding the correlation scores, and we demonstrate the practicality of the scheme through experiments with real data.

Keywords

Privacy, Verifiability, Collusion-resistance, Correlation computation, Vector product

Discipline

Information Security | Software Engineering

Research Areas

Cybersecurity

Publication

Information Sciences

Volume

420

First Page

345

Last Page

363

ISSN

0020-0255

Identifier

10.1016/j.ins.2017.08.068

Publisher

Elsevier

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org./10.1016/j.ins.2017.08.068

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