While cloud computing has exploded in popularity in recent years thanks to the potential efficiency and cost savings of outsourcing the storage and management of data and applications, a number of vulnerabilities that led to multiple attacks have deterred many potential users.As a result, experts in the field argued that new mechanisms are needed in order to create trusted and secure cloud services. Such mechanisms would eradicate the suspicion of users towards cloud computing by providing the necessary security guarantees. Searchable Encryption is among the most promising solutions -one that has the potential to help offer truly secure and privacypreserving cloud services. We start this paper by surveying the most important searchable encryption schemes and their relevance to cloud computing. In light of this analysis we demonstrate the inefficiencies of the existing schemes and expand our analysis by discussing certain confidentiality and privacy issues. Further, we examine how to integrate such a scheme with a popular cloud platform. Finally, we have chosen -based on the findings of our analysis -an * Corresponding Author.
Preprint submitted to Computer Science Review JournalAugust 14, 2017 existing scheme and implemented it to review its practical maturity for deployment in real systems. The survey of the field, together with the analysis and with the extensive experimental results provides a comprehensive review of the theoretical and practical aspects of searchable encryption.
Abstract-The information filter has evolved into a key tool for distributed and decentralized multisensor estimation and control. Essentially, it is an algebraical reformulation of the Kalman filter and provides estimates on the information about an uncertain state rather than on a state itself. Whereas many practicable Kalman filtering techniques for nonlinear system and sensor models have been developed, approaches towards nonlinear information filtering are still scarce and limited. In order to deal with nonlinear systems and sensors, this paper derives an approximation technique for arbitrary probability densities that provides the same distributable fusion structure as the linear information filter. The presented approach not only constitutes a nonlinear version of the information filter, but it also points the direction to a Hilbert space structure on probability densities, whose vector space operations correspond to the fusion and weighting of information.
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