東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Scalable Privacy-Preserving Particip...

Li, Ting.

Linked to FindBook

Google Book

Amazon

博客來

Scalable Privacy-Preserving Participant Selection With Appropriate Incentive in Mobile Crowdsensing Systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Scalable Privacy-Preserving Participant Selection With Appropriate Incentive in Mobile Crowdsensing Systems./
Author:	Li, Ting.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	104 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-10, Section: B.
Contained By:	Dissertations Abstracts International80-10B.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13810064
ISBN:	9781392029046

Scalable Privacy-Preserving Participant Selection With Appropriate Incentive in Mobile Crowdsensing Systems.
Li, Ting.

Scalable Privacy-Preserving Participant Selection With Appropriate Incentive in Mobile Crowdsensing Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 104 p.

Source: Dissertations Abstracts International, Volume: 80-10, Section: B.

Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2019.

This item must not be sold to any third party vendors.

Mobile crowdsensing (MCS) has been emerging as a new sensing paradigm where vast numbers of mobile devices are used for sensing and collecting data in various applica- tions. Unlike traditional sensor networks (or the static sensing paradigm), which use pre-deployed sensors to collect specific information at fixed locations, MCS leverages a large number of participants (smart mobile device users) to jointly perform sensing and other crowd sourcing tasks. The MCS solution brings several advantages, includ- ing low infrastructure cost, real-time and wide coverage, and potential integration with human intelligence. However, it also faces several research challenges. These in- clude participant selection, incentive mechanisms, and privacy protection and so on. In this work, we mainly focus on designing scalable privacy-preserving participant selection with appropriate incentive for mobile crowdsensing system. Auction based participant selection has been widely used for current MCS systems to achieve user incentive and task assignment optimization. However, participant se- lection problems solved with auction-based approaches usually involve participants' privacy concerns because a participant's bids may contain her private information, and disclosure of participants' bids may disclose their private information as well. Following the classical VCG auction, we carefully design a scalable grouping based privacy-preserving participant selection scheme, which uses Lagrange polynomial in- terpolation (LPI) to perturb participants? bids within groups. The proposed solution, which built on the current MCS platform, can protect such bid privacy in a tempo- rally and spatially dynamic MCS system. Later, we analyze the bidding game of our proposed solution with three implications to prove the security. To address the participant grouping problem with the constraint of communication cost during participant bidding process, we propose two algorithms: sorting and dynamic programming (DP). We prove that sorting algorithm could efficiently achieve a feasible solution with a certain approximation ratio for different problems, while dynamic programming algorithm is proved to provide the optimal solution. However, the selection scheme with cloud-based MCS platform suffers from high overheads, poor scalability and more important. To address this issue and to enhance the protection of user privacy, we further propose a set of novel privacy-preserving grouping methods, which place participants into small groups over hierarchical edge clouds. Our design goal is to group participants in a way that minimizes the com- munication cost during secure sharing/bidding, while satisfying each participant's requirement for privacy preservation. For different scenarios and optimization functions, we propose a set of grouping schemes to fulfill this goal. For all of above work, extensive simulations over both synthetic and real-life datasets are conducted to verify the efficiency and security, and confirm the effectiveness of proposed mechanisms.

ISBN: 9781392029046Subjects--Topical Terms:

523869
Computer science.

Scalable Privacy-Preserving Participant Selection With Appropriate Incentive in Mobile Crowdsensing Systems.
LDR:04167nmm a2200313 4500 001 2208817
005 20191025102422.5
008 201008s2019 ||||||||||||||||| ||eng d
020 $a 9781392029046
035 $a (MiAaPQ)AAI13810064
035 $a (MiAaPQ)uncc:11985
035 $a AAI13810064
040 $a MiAaPQ $c MiAaPQ
100 1 $a Li, Ting. $3 3188372
245 1 0 $a Scalable Privacy-Preserving Participant Selection With Appropriate Incentive in Mobile Crowdsensing Systems.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 104 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-10, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Wang, YU.
502 $a Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Mobile crowdsensing (MCS) has been emerging as a new sensing paradigm where vast numbers of mobile devices are used for sensing and collecting data in various applica- tions. Unlike traditional sensor networks (or the static sensing paradigm), which use pre-deployed sensors to collect specific information at fixed locations, MCS leverages a large number of participants (smart mobile device users) to jointly perform sensing and other crowd sourcing tasks. The MCS solution brings several advantages, includ- ing low infrastructure cost, real-time and wide coverage, and potential integration with human intelligence. However, it also faces several research challenges. These in- clude participant selection, incentive mechanisms, and privacy protection and so on. In this work, we mainly focus on designing scalable privacy-preserving participant selection with appropriate incentive for mobile crowdsensing system. Auction based participant selection has been widely used for current MCS systems to achieve user incentive and task assignment optimization. However, participant se- lection problems solved with auction-based approaches usually involve participants' privacy concerns because a participant's bids may contain her private information, and disclosure of participants' bids may disclose their private information as well. Following the classical VCG auction, we carefully design a scalable grouping based privacy-preserving participant selection scheme, which uses Lagrange polynomial in- terpolation (LPI) to perturb participants? bids within groups. The proposed solution, which built on the current MCS platform, can protect such bid privacy in a tempo- rally and spatially dynamic MCS system. Later, we analyze the bidding game of our proposed solution with three implications to prove the security. To address the participant grouping problem with the constraint of communication cost during participant bidding process, we propose two algorithms: sorting and dynamic programming (DP). We prove that sorting algorithm could efficiently achieve a feasible solution with a certain approximation ratio for different problems, while dynamic programming algorithm is proved to provide the optimal solution. However, the selection scheme with cloud-based MCS platform suffers from high overheads, poor scalability and more important. To address this issue and to enhance the protection of user privacy, we further propose a set of novel privacy-preserving grouping methods, which place participants into small groups over hierarchical edge clouds. Our design goal is to group participants in a way that minimizes the com- munication cost during secure sharing/bidding, while satisfying each participant's requirement for privacy preservation. For different scenarios and optimization functions, we propose a set of grouping schemes to fulfill this goal. For all of above work, extensive simulations over both synthetic and real-life datasets are conducted to verify the efficiency and security, and confirm the effectiveness of proposed mechanisms.
590 $a School code: 0694.
650 4 $a Computer science. $3 523869
690 $a 0984
710 2 $a The University of North Carolina at Charlotte. $b Computer Science. $3 3181506
773 0 $t Dissertations Abstracts International $g 80-10B.
790 $a 0694
791 $a Ph.D.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13810064