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Big Data Privacy Preservation for Cyber-Physical Systems

  • Miao Pan
  • Jingyi Wang
  • Sai Mounika Errapotu
  • Xinyue Zhang
  • Jiahao Ding
  • Zhu Han

Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Miao Pan, Jingyi Wang, Sai Mounika Errapotu, Xinyue Zhang, Jiahao Ding, Zhu Han
    Pages 1-10
  3. Miao Pan, Jingyi Wang, Sai Mounika Errapotu, Xinyue Zhang, Jiahao Ding, Zhu Han
    Pages 11-20
  4. Miao Pan, Jingyi Wang, Sai Mounika Errapotu, Xinyue Zhang, Jiahao Ding, Zhu Han
    Pages 21-34
  5. Miao Pan, Jingyi Wang, Sai Mounika Errapotu, Xinyue Zhang, Jiahao Ding, Zhu Han
    Pages 35-45
  6. Miao Pan, Jingyi Wang, Sai Mounika Errapotu, Xinyue Zhang, Jiahao Ding, Zhu Han
    Pages 47-55
  7. Miao Pan, Jingyi Wang, Sai Mounika Errapotu, Xinyue Zhang, Jiahao Ding, Zhu Han
    Pages 57-73

About this book

Introduction

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 cryptographic techniques and differential privacy in this brief. This brief also focuses on effectively integrating the data analysis and data privacy preservation techniques to provide the most desirable solutions for the state-of-the-art CPS with various application-specific requirements.  

Cyber-physical systems (CPS) are the “next generation of engineered systems,” that integrate computation and networking capabilities to monitor and control entities in the physical world. Multiple domains of CPS typically collect huge amounts of data and rely on it for decision making, where the data may include individual or sensitive information, for e.g., smart metering, intelligent transportation, healthcare, sensor/data aggregation, crowd sensing etc. This brief assists users working in these areas and contributes to the literature by addressing data privacy concerns during collection, computation or big data analysis in these large scale systems. Data breaches result in undesirable loss of privacy for the participants and for the entire system, therefore identifying the vulnerabilities and developing tools to mitigate such concerns is crucial to build high confidence CPS.

This Springerbrief targets professors, professionals and research scientists working in Wireless Communications, Networking, Cyber-Physical Systems and Data Science. Undergraduate and graduate-level  students interested in Privacy Preservation of state-of-the-art Wireless Networks and Cyber-Physical Systems will use this Springerbrief as a study guide.  


Keywords

Big Data Analytics Wireless big data privacy differential privacy data-driven optimization ADMM machine learning Cyber-Physical Systems Privacy Preservation Applied Crypography Data Analysis Secure Computation Smart Grid Information-Centric Network Spectrum Tading Cognitive Radio Network Clock-Auction Demand Response Colocation Data Centers

Authors and affiliations

  • Miao Pan
    • 1
  • Jingyi Wang
    • 2
  • Sai Mounika Errapotu
    • 3
  • Xinyue Zhang
    • 4
  • Jiahao Ding
    • 5
  • Zhu Han
    • 6
  1. 1.Department of Electrical and Computer EngineeringUniversity of HoustonHoustonUSA
  2. 2.Department of Electrical and Computer EngineeringUniversity of HoustonHoustonUSA
  3. 3.Department of Electrical and Computer EngineeringUniversity of HoustonHoustonUSA
  4. 4.Department of Electrical and Computer EngineeringUniversity of HoustonHoustonUSA
  5. 5.Department of Electrical and Computer EngineeringUniversity of HoustonHoustonUSA
  6. 6.Department of Electrical and Computer EngineeringUniversity of HoustonHoustonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-13370-2
  • Copyright Information The Author(s), under exclusive license to Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-030-13369-6
  • Online ISBN 978-3-030-13370-2
  • Series Print ISSN 2191-8112
  • Series Online ISSN 2191-8120
  • Buy this book on publisher's site