Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Authenticating aggregate queries over set-valued data with confidentiality

Language

English

Abstract

With recent advances in data-as-a-service (DaaS) and cloud computing, aggregate query services over set-valued data are becoming widely available for business intelligence that drives decision making. However, as the service provider is often a third-party delegate of the data owner, the integrity of the query results cannot be guaranteed and is thus imperative to be authenticated. Unfortunately, existing query authentication techniques either do not work for set-valued data or they lack data confidentiality. In this paper, we propose authenticated aggregate queries over set-valued data that not only ensure the integrity of query results but also preserve the confidentiality of source data. As many aggregate queries are composed of multiset operations such as set union and subset, we first develop a family of privacy-preserving authentication protocols for primitive multiset operations. Using these protocols as building blocks, we present a privacy-preserving authentication framework for various aggregate queries and further optimize their authentication performance. Security analysis and empirical evaluation show that our proposed privacy-preserving authentication techniques are feasible and robust under a wide range of system workloads.

Keywords

Query Authentication, Aggregate Queries, Set-Valued Data, Merkle Hash Tree, Aggregates, Authentication, Genomics, Bioinformatics, Protocols, Indexes, Query processing

Publication Date

2017

Source Publication Title

IEEE Transactions on Knowledge and Data Engineering

Publisher

Institute of Electrical and Electronics Engineers

Peer Reviewed

1

Funder

This work was supported by Research Grants Council (RGC) of Hong Kong under GRF Projects 12244916, 15238116, 12202414, 12200114, 12200914, CRF Project C1008-16G, and NSFC Grant 61370231.

DOI

10.1109/TKDE.2017.2773541

Link to Publisher's Edition

http://dx.doi.org/10.1109/TKDE.2017.2773541

ISSN (print)

10414347

ISSN (electronic)

15582191

This document is currently not available here.

Share

COinS