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.
