Authors:
- This book addresses the processing limitations of massive data without disclosing users’ privacy in various CPS applications, a major concern in real-time big data analytics to achieve better data management and effective decision making
Part of the book series: SpringerBriefs in Electrical and Computer Engineering (BRIEFSELECTRIC)
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Table of contents (6 chapters)
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Front Matter
About this book
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
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Department of Electrical and Computer Engineering, University of Houston, Houston, USA
Miao Pan, Jingyi Wang, Sai Mounika Errapotu, Xinyue Zhang, Jiahao Ding, Zhu Han
Bibliographic Information
Book Title: Big Data Privacy Preservation for Cyber-Physical Systems
Authors: Miao Pan, Jingyi Wang, Sai Mounika Errapotu, Xinyue Zhang, Jiahao Ding, Zhu Han
Series Title: SpringerBriefs in Electrical and Computer Engineering
DOI: https://doi.org/10.1007/978-3-030-13370-2
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-030-13369-6Published: 04 April 2019
eBook ISBN: 978-3-030-13370-2Published: 25 March 2019
Series ISSN: 2191-8112
Series E-ISSN: 2191-8120
Edition Number: 1
Number of Pages: IX, 73
Number of Illustrations: 2 b/w illustrations, 23 illustrations in colour
Topics: Wireless and Mobile Communication, Security, Communications Engineering, Networks