Data-as-a-Service (DaaS) is a paradigm that provides data on demand to consumers
across different cloud platforms over the Internet. Yet, a single DaaS provider may not be
able to fulfill a data request. Consequently, the concept of DaaS mashup was introduced to
enable DaaS 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 thesis, 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. We propose a greedy algorithm to determine
a suitable group of DaaS providers whose data can satisfy a given request. Furthermore,
our framework securely integrates the data from multiple DaaS providers while preserving
the privacy of the resulting mashup data. Experiments on real-life data demonstrate that
our DaaS mashup framework is scalable to large set of databases and it can efficiently and effectively satisfy the data privacy and data mining requirements specified by the DaaS
providers and the data consumers
