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Titlebook: Big Data Privacy Preservation for Cyber-Physical Systems; Miao Pan,Jingyi Wang,Zhu Han Book 2019 The Author(s), under exclusive license to

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發(fā)表于 2025-3-21 17:49:25 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Big Data Privacy Preservation for Cyber-Physical Systems
影響因子2023Miao Pan,Jingyi Wang,Zhu Han
視頻videohttp://file.papertrans.cn/186/185656/185656.mp4
發(fā)行地址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 man
學(xué)科分類SpringerBriefs in Electrical and Computer Engineering
圖書封面Titlebook: Big Data Privacy Preservation for Cyber-Physical Systems;  Miao Pan,Jingyi Wang,Zhu Han Book 2019 The Author(s), under exclusive license to
影響因子.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
Pindex Book 2019
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發(fā)表于 2025-3-21 22:22:13 | 只看該作者
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,Spectrum Trading with Secondary Users’ Privacy Preservation,uncertain and individual SU’s traffic portfolio contains private information. In this chapter, we propose a data-driven spectrum trading scheme which maximizes PUs’ revenue and preserves SUs’ demand differential privacy. Briefly, we introduce a novel network architecture consisting of the PSP, the S
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Clock Auction Inspired Privacy Preservation in Colocation Data Centers,ption during emergency situations to prevent major economic losses. While existing literature concentrates on owner-operated data centers (e.g., Google), this work studies EDR in multi-tenant colocation data centers (e.g., Equinix) where servers are owned and managed by individual tenants and which
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發(fā)表于 2025-3-22 18:13:38 | 只看該作者
2191-8112 n in real-time big data analytics to achieve better data man.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 methodologi
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發(fā)表于 2025-3-23 02:15:45 | 只看該作者
Introduction to Complex Analysise present four different applications of CPS in the real world and briefly summarize the state-of-art research and the research challenges for the privacy preservation and big data analysis in these CPS systems. In this chapter, we provide content outlines for the rest of the book.
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發(fā)表于 2025-3-23 08:55:17 | 只看該作者
https://doi.org/10.1007/978-3-642-61525-2t CPS. The concept of . was first proposed by Dwork (Differential privacy: a survey of results. In: International conference on theory and applications of models of computation, Xi’an, 2008) which specifies that any individual has a very small influence on the (distribution of the) outcome of the co
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