Salah-Eddine Tbahriti

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Data as a Service (DaaS) builds on service-oriented technologies to enable fast access to data resources on the Web. However, this paradigm raises several new concerns that traditional privacy models for Web services do not handle. First, the distinction between the roles of service providers and data providers is unclear, leaving the latter helpless for(More)
Data as a Service (DaaS) builds on service-oriented technologies to enable fast access to data resources on the Web. However, this paradigm raises several new privacy concerns that traditional privacy models do not handle. In addition, DaaS composition may reveal privacy-sensitive information. In this paper, we propose a formal privacy model in order to(More)
Rich types of data offered by data as a service(DaaS) in the cloud are typically associated with different and complex data concerns that DaaS service providers, data providers and data consumers must carefully examine and agree with before passing and utilizing data. Unlike service agreements, data agreements, reflecting conditions established on the basis(More)
Data Mashup is a special class of mashup application that combines information on the fly from multiple data sources to respond to transient business needs. Data mashup is a difficult task that would require an important programming skill on the side of mashups' creators, and involves handling many challenging privacy and security concerns raised by data(More)
Data as a Service (DaaS) builds on service-oriented technologies to enable fast access to data resources on the Web. However, this paradigm raises several new privacy concerns that traditional privacy models do not handle since they only focus on the service interface without taking into account privacy constraints related to the data exchanged with a DaaS(More)
In this paper, we present a formal model for preserving privacy in Web services. We define a Web service-aware privacy model that deals with the privacy of input data, output data, and operation usage. We introduce a matching protocol that caters for partial and total privacy compatibility. We propose also a negotiation model to reconcile clients'(More)
In this paper, we present Meerkat, a dynamic framework for preserving privacy in Web services. We define a Web service-aware privacy model that deals with the privacy of input data, output data, and operation usage. We introduce a matching protocol that caters for partial and total privacy compatibility. Finally, we propose a negotiation model to reconcile(More)
Data Mashup is a special class of mashup application that combines information on the fly from multiple data sources to respond to transient business needs. In this paper, we propose two optimization algorithms to optimize Data Mashups. The first allows for selecting the minimum number of services required in the data mashup. The second exploits the(More)
Privacy is still among the key challenges that keep hampering DaaS service composition solution. Indeed services may follow different, conflicting privacy specifications with respect to the data they use and provide within a composition. In this paper, we propose an approach for privacy-aware composition of DaaS services. Our approach allows verifying the(More)