D-Mash: A Framework for Privacy-Preserving Data-as-a-Service Mashups

Abstract

Data-as-a-Service (DaaS) mashup enables data providers to dynamically integrate their data on demand depending on consumers' requests. Utilizing DaaS mashup, however, involves some challenges. Mashing up data from multiple sources to answer a consumer's request might reveal sensitive information and thereby compromise the privacy of individuals. Moreover, data integration of arbitrary DaaS providers might not always be sufficient to answer incoming requests. In this paper, we provide a cloud-based framework for privacy-preserving DaaS mashup that enables secure collaboration between DaaS providers for the purpose of generating an anonymous dataset to support data mining. Experiments on real-life data demonstrate that our DaaS mashup framework is scalable and can efficiently and effectively satisfy the data privacy and data mining requirements specified by the DaaS providers and the data consumers.