Enabling Secure Trustworthiness Assessment and Privacy Protection in Integrating Data for Trading Person-Specific Information

Document Type

Article

Source of Publication

IEEE Transactions on Engineering Management

Publication Date

1-1-2020

Abstract

IEEE With increasing adoption of cloud services in the e-market, collaboration between stakeholders is easier than ever. Consumer stakeholders demand data from various sources to analyze trends and improve customer services. Data-as-a-service enables data integration to serve the demands of data consumers. However, the data must be of good quality and trustful for accurate analysis and effective decision making. In addition, a data custodian or provider must conform to privacy policies to avoid potential penalties for privacy breaches. To address these challenges, we propose a twofold solution: 1) we present the first information entropy-based trust computation algorithm, IEB_Trust, that allows a semitrusted arbitrator to detect the covert behavior of a dishonest data provider and chooses the qualified providers for a data mashup and 2) we incorporate the Vickrey–Clarke–Groves (VCG) auction mechanism for the valuation of data providers’ attributes into the data mashup process. Experiments on real-life data demonstrate the robustness of our approach in restricting dishonest providers from participation in the data mashup and improving the efficiency in comparison to provenance-based approaches. Furthermore, we derive the monetary shares for the chosen providers from their information utility and trust scores over the differentially private release of the integrated dataset under their joint privacy requirements.

ISSN

0018-9391

Publisher

Institute of Electrical and Electronics Engineers Inc.

Volume

68

First Page

149

Last Page

169

Disciplines

Computer Sciences

Keywords

Cloud computing, data mashup, data privacy, data trustworthiness, monetary valuation

Scopus ID

85081407270

Indexed in Scopus

yes

Open Access

no

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