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008			230324s2023 si | o |||| 0|eng d
020			_a9789811983153
024	7		_a10.1007/978-981-19-8315-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .A25 _b2023 EB
100	1		_aZhang, Chuan _eautor _4http://id.loc.gov/vocabulary/relators/aut _9689360
245	1	0	_aPrivacy-Preserving in Mobile Crowdsensing _cby Chuan Zhang, Tong Wu, Youqi Li, Liehuang Zhu
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aPart I. Overview and Basic Concept of Mobile Crowdsensing Technology -- Chapter 1. Introduction -- Chapter 2. Overview of Mobile Crowdsensing Technology -- Part II. Privacy-Preserving Task Allocation -- Chapter 3. Privacy-Preserving Content based Task Allocation -- Chapter 4. Privacy-Preserving Location based Task Allocation -- Part III. Privacy-Preserving Truth Discovery -- Chapter 5. Privacy-Preserving Truth Discovery with Truth Transparency -- Chapter 6. Privacy-Preserving Truth Discovery with Truth Hiding -- Chapter 7. Privacy-Preserving Truth Discovery with Task Hiding -- Part IV. Summary and Future Research Directions -- Chapter 8. Summary.
520			_aMobile crowdsensing is a new sensing paradigm that utilizes the intelligence of a crowd of individuals to collect data for mobile purposes by using their portable devices, such as smartphones and wearable devices. Commonly, individuals are incentivized to collect data to fulfill a crowdsensing task released by a data requester. This "sensing as a service" elaborates our knowledge of the physical world by opening up a new door of data collection and analysis. However, with the expansion of mobile crowdsensing, privacy issues urgently need to be solved. In this book, we discuss the research background and current research process of privacy protection in mobile crowdsensing. In the first chapter, the background, system model, and threat model of mobile crowdsensing are introduced. The second chapter discusses the current techniques to protect user privacy in mobile crowdsensing. Chapter three introduces the privacy-preserving content-based task allocation scheme. Chapter four further introduces the privacy-preserving location-based task scheme. Chapter five presents the scheme of privacy-preserving truth discovery with truth transparency. Chapter six proposes the scheme of privacy-preserving truth discovery with truth hiding. Chapter seven summarizes this monograph and proposes future research directions. In summary, this book introduces the following techniques in mobile crowdsensing: 1) describe a randomizable matrix-based task-matching method to protect task privacy and enable secure content-based task allocation; 2) describe a multi-clouds randomizable matrix-based task-matching method to protect location privacy and enable secure arbitrary range queries; and 3) describe privacy-preserving truth discovery methods to support efficient and secure truth discovery. These techniques are vital to the rapid development of privacy-preserving in mobile crowdsensing.
988			_aSpringer_Computer_2023
650		7	_2embne _9158200 _aSeguridad informática
700	1		_9690174 _aWu, Tong _eautor
700	1		_9689357 _aLi, Youqi _eautor
700	1		_9690173 _aZhu, Liehuang _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-8315-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2024 _dz _ek _zSI