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

Linked to FindBook

Google Book

Amazon

博客來

Behavior-Based Probabilistic User Identification Through Passive Sensing.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Behavior-Based Probabilistic User Identification Through Passive Sensing./
Author:	Wang, Xiao.
Description:	1 online resource (90 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 79-04, Section: B.
Contained By:	Dissertations Abstracts International79-04B.
Subject:	Computer engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10601222click for full text (PQDT)
ISBN:	9780355116199

Behavior-Based Probabilistic User Identification Through Passive Sensing.
Wang, Xiao.

Behavior-Based Probabilistic User Identification Through Passive Sensing. - 1 online resource (90 pages)

Source: Dissertations Abstracts International, Volume: 79-04, Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2017.

Includes bibliographical references

A notion of identity is of vital importance to each individual and the society. Its significance is manifest from two perspectives. On one hand, many systems rely on identity to provide proper services and fulfill their functionality. On the other hand, loss or leakage of identity can cause disastrous consequences. Identity is hence the sought-after to both benign and malicious entities. The representation, acquisition, validation and application of identity have been evolving consistently with the advances in technology. The last decade has witnessed the rapid adoption of mobile electronic devices and the emergence of Internet-of-Things hardware. Their onboard solid-state sensors enable ubiquitous sensing of user, environment and the interaction between them, accumulating ample sensory data that can be leveraged for user identification. In this dissertation, we study behavior-based user identification through passive sensing and its application in new real-world scenarios. We apply statistical modeling methods to the sensory measurements and extract sufficient entropy to establish user identity. A corresponding behavior-based identification framework is specified with necessary components and steps. We contrast the behavior-based approach to existing mechanisms and highlight its advantages. To demonstrate the practical implications of the proposed approach, we propose two application scenarios in the mobile and IoT realms, and conduct experiments. The mobile application investigates user recognition across mobile devices. We leverage the way users interact with mobile apps to track them even when they switch between multiple mobile devices. The IoT application, following the emergence of IoT- equipped buildings, studies room-level indoor localization. We model residents' mobility patterns through occupancy measurements and further learn their locations. Our experimental results demonstrate the effectiveness of identifying users through behavioral patterns under distinct application scenarios.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9780355116199Subjects--Topical Terms:

621879
Computer engineering.
Subjects--Index Terms:

Passive sensingIndex Terms--Genre/Form:

542853
Electronic books.

Behavior-Based Probabilistic User Identification Through Passive Sensing.
LDR:03351nmm a2200361K 4500 001 2358012
005 20230725094924.5
006 m o d
007 cr mn ---uuuuu
008 241011s2017 xx obm 000 0 eng d
020 $a 9780355116199
035 $a (MiAaPQ)AAI10601222
035 $a (MiAaPQ)cmu:10124
035 $a AAI10601222
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Wang, Xiao. $3 1292882
245 1 0 $a Behavior-Based Probabilistic User Identification Through Passive Sensing.
264 0 $c 2017
300 $a 1 online resource (90 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 79-04, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Tague, Patrick.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2017.
504 $a Includes bibliographical references
520 $a A notion of identity is of vital importance to each individual and the society. Its significance is manifest from two perspectives. On one hand, many systems rely on identity to provide proper services and fulfill their functionality. On the other hand, loss or leakage of identity can cause disastrous consequences. Identity is hence the sought-after to both benign and malicious entities. The representation, acquisition, validation and application of identity have been evolving consistently with the advances in technology. The last decade has witnessed the rapid adoption of mobile electronic devices and the emergence of Internet-of-Things hardware. Their onboard solid-state sensors enable ubiquitous sensing of user, environment and the interaction between them, accumulating ample sensory data that can be leveraged for user identification. In this dissertation, we study behavior-based user identification through passive sensing and its application in new real-world scenarios. We apply statistical modeling methods to the sensory measurements and extract sufficient entropy to establish user identity. A corresponding behavior-based identification framework is specified with necessary components and steps. We contrast the behavior-based approach to existing mechanisms and highlight its advantages. To demonstrate the practical implications of the proposed approach, we propose two application scenarios in the mobile and IoT realms, and conduct experiments. The mobile application investigates user recognition across mobile devices. We leverage the way users interact with mobile apps to track them even when they switch between multiple mobile devices. The IoT application, following the emergence of IoT- equipped buildings, studies room-level indoor localization. We model residents' mobility patterns through occupancy measurements and further learn their locations. Our experimental results demonstrate the effectiveness of identifying users through behavioral patterns under distinct application scenarios.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Computer engineering. $3 621879
653 $a Passive sensing
653 $a User identification
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0464
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Carnegie Mellon University. $b Electrical and Computer Engineering. $3 2094139
773 0 $t Dissertations Abstracts International $g 79-04B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10601222 $z click for full text (PQDT)