In light of the increasing reliance on Small Satellites for Earth Observation, ensuring the security of satellite imagery is paramount, particularly given their limited onboard resources. In this paper, we introduce a security model that formally and semantically specifies the security requirements for satellite imagery. Building on this foundation, we propose a secure encryption protocol that ranks images by security importance, filtering them according to predefined specifications so that only those necessitating protection are selected. This dual-stage process enables selective control and optimal resource allocation, while our fast encryption algorithm is explicitly designed to be aware of onboard resource constraints and incorporates a remote-control mechanism to dynamically adjust security parameters.

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 extend DaaS descriptions with privacy capabilities. The privacy model allows a service to define a privacy policy and a set of privacy requirements. We also propose a privacy-preserving DaaS composition approach allowing to verify the compatibility between privacy requirements and policies in DaaS composition. We propose a negotiation mechanism that makes it possible to dynamically reconcile the privacy capabilities of services when incompatibilities arise in a composition. We validate the applicability of our proposal through a prototype implementation and a set of experiments.

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 compatibility of privacy specifications of services involved in a composition. In the case when any composition will be incompatible in terms of privacy, we introduce a novel approach based on negotiation to reach compatibility of concerned services. The negotiation approach is cautiously operated with without any privacy damaging of services. We validate the applicability of our proposal through a set of experiments.

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 of data concerns, between relevant stakeholders have got little attention. However, as data concerns are complex and contextual, given the trend of mixing data sources by automated techniques, such as data mash up, data agreements must be associated with data discovery, retrieval and utilization. Unfortunately, exchanging data agreements so far has not been automated and incorporated into service and data discovery and composition. In this paper, we analyze possible steps and propose interactions among data consumers, DaaS service providers and data providers in exchanging data agreements. Based on that, we present a novel service for composing, managing, analyzing data agreements for DaaS in cloud environments and data marketplaces.

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 providers. This situation prevents non-expert users from mashing up data at large. In this paper, we present a declarative approach for mashing-up data. The approach allows data mashup creators to build data mashups without any programming involved. The approach builds the mashups automatically and takes into account the data's privacy concerns. We evaluate the efficiency of the approach via a thorough set of experiments. The results show that handling data privacy introduces only a negligible cost in the mashup building time.

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 clients' requirements with providers' policies in case of incompatibility.