Big Data Privacy Preservation for Cyber-Physical Systems

This SpringerBrief mainly focuses on effective big data analytics for CPS, and addresses the privacy issues that arise on various CPS applications. The authors develop a series of privacy preserving data analytic and processing methodologies through data driven optimization based on applied cryptogr...

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Κύριοι συγγραφείς: Pan, Miao (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Wang, Jingyi (http://id.loc.gov/vocabulary/relators/aut), Errapotu, Sai Mounika (http://id.loc.gov/vocabulary/relators/aut), Zhang, Xinyue (http://id.loc.gov/vocabulary/relators/aut), Ding, Jiahao (http://id.loc.gov/vocabulary/relators/aut), Han, Zhu (http://id.loc.gov/vocabulary/relators/aut)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
Έκδοση:1st ed. 2019.
Σειρά:SpringerBriefs in Electrical and Computer Engineering,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • Chapter 1 Cyber-Physical Systems
  • Chapter 2 Preliminaries
  • Chapter 3 Spectrum Trading with Secondary Users' Privacy Protection
  • Chapter 4 Optimization for Utility Providers with Privacy Preservation of Users' Energy Profile
  • Chapter 5 Caching with Users' Differential Privacy Preservation in Information-Centric Networks
  • Chapter 6 Clock Auction Inspired Privacy Preservation in Colocation Data Centers.