Privacy Preserving Data-as-a-Service Mashups

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

Fusion: Privacy-preserving distributed protocol for high-dimensional data mashup

© 2015 IEEE. In the last decade, several approaches concerning private data release for data mining have been proposed. Data mashup, on the other hand, has recently emerged as a mechanism for integrating data from several data providers. Fusing both techniques to generate mashup data in a distributed environment while providing privacy and utility guarantees on the output involves several challenges. That is, how to ensure that no unnecessary information is leaked to the other parties during the mashup process, how to ensure the mashup data is protected against certain privacy threats, and how to handle the high-dimensional nature of the mashup data while guaranteeing high data utility. In this paper, we present Fusion, a privacy-preserving multi-party protocol for data mashup with guaranteed LKC-privacy for the purpose of data mining. Experiments on real-life data demonstrate that the anonymous mashup data provide better data utility, the approach can handle high dimensional data, and it is scalable with respect to the data size