東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Big data- Security measures." ]

切換: 標籤 | MARC模式 | ISBD

Big data privacy preservation for cy...

SpringerLink (Online service)

FindBook

Google Book

Amazon

博客來

Big data privacy preservation for cyber-physical systems

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Big data privacy preservation for cyber-physical systems/ by Miao Pan ... [et al.].
其他作者:	Pan, Miao.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	ix, 73 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Big data - Security measures. -
電子資源:	https://doi.org/10.1007/978-3-030-13370-2
ISBN:	9783030133702

Big data privacy preservation for cyber-physical systems
Big data privacy preservation for cyber-physical systems[electronic resource] /by Miao Pan ... [et al.]. - Cham :Springer International Publishing :2019. - ix, 73 p. :ill., digital ;24 cm. - SpringerBriefs in electrical and computer engineering,2191-8112. - SpringerBriefs in electrical and computer engineering..

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.

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.

ISBN: 9783030133702

Standard No.: 10.1007/978-3-030-13370-2doiSubjects--Topical Terms:

3310623
Big data
--Security measures.

LC Class. No.: QA76.9.A25

Dewey Class. No.: 005.7

Big data privacy preservation for cyber-physical systems
LDR:03281nmm a2200337 a 4500 001 2180537
003 DE-He213
005 20190325113759.0
006 m d
007 cr nn 008maaau
008 191122s2019 gw s 0 eng d
020 $a 9783030133702 $q (electronic bk.)
020 $a 9783030133696 $q (paper)
024 7 $a 10.1007/978-3-030-13370-2 $2 doi
035 $a 978-3-030-13370-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.A25
072 7 $a TJKW $2 bicssc
072 7 $a TEC061000 $2 bisacsh
072 7 $a TJKW $2 thema
082 0 4 $a 005.7 $2 23
090 $a QA76.9.A25 $b B592 2019
245 0 0 $a Big data privacy preservation for cyber-physical systems $h [electronic resource] / $c by Miao Pan ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a ix, 73 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in electrical and computer engineering, $x 2191-8112
505 0 $a 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.
520 $a 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.
650 0 $a Big data $x Security measures. $3 3310623
650 0 $a Cooperating objects (Computer systems) $3 2055414
650 1 4 $a Wireless and Mobile Communication. $3 3338159
650 2 4 $a Security. $3 3134865
650 2 4 $a Communications Engineering, Networks. $3 891094
700 1 $a Pan, Miao. $3 2165488
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in electrical and computer engineering. $3 1565565
856 4 0 $u https://doi.org/10.1007/978-3-030-13370-2
950 $a Engineering (Springer-11647)