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Author(s) -
Abderrahim Benslimane
Publication year - 2016
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/elps.201670145
Subject(s) - focus (optics) , computer science , citation , world wide web , physics , optics
Special Issue on Signal Processing for Cyber-security and Privacy Aims and Scope Information technology and electronic communications have been rapidly applied to many spheres of human activity, including commerce, medicine and social networking. This has led to the creation of massive electronic repositories for distributed information storage and processing, which enables access by a large number of authorized users. The need for timely access to electronic data makes it imperative to guarantee the security and privacy of this data. Traditionally, electronic data security has been ensured via cryptographic techniques, but these distributed data systems require security and privacy mechanisms at all levels of the system. Thus, providing precise guarantees on the security and privacy of electronic information requires leveraging a range of information processing techniques beyond traditional cryptography to ensure secure distributed storage and access mechanisms. The problems of information exchange, interaction, and access lend themselves to fundamental information processing abstractions and theoretical analysis. The tools of rate-distortion theory, distributed compression algorithms, distributed storage codes, machine learning for feature identification and suppression, and compressive sensing and sampling theory are fundamental and can be applied to precisely formulate and quantify the tradeoff between utility and privacy in a variety of domains. Thus, while rate-distortion theory and information-theoretic security can provide fundamental bounds on privacy and security leakage of distributed data systems, the information and signal processing techniques of compressive sensing, machine learning, and graphical models are the key ingredients necessary to achieve these performance limits in a variety of applications involving streaming data (smart grid, intelligent data collection), distributed data storage (cloud), and interactive data applications across a number of platforms. This special issue seeks to provide a venue for ongoing research in information and signal processing for security and privacy applications across a wide variety of domains, including communication media (e.g. ranging from wireless networks at the edge to optical backbones at the core of the Internet), to computer systems (e.g. ranging from traditional computer architectures to distributed systems, including cloud computing).

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