國立虎尾科技大學 |

Assessing and Enhancing Users' Online Data Privacy.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Assessing and Enhancing Users' Online Data Privacy./
作者:	Khandelwal, Rishabh.
面頁冊數:	1 online resource (236 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798384059516

Assessing and Enhancing Users' Online Data Privacy.
Khandelwal, Rishabh.

Assessing and Enhancing Users' Online Data Privacy. - 1 online resource (236 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2024.

Includes bibliographical references

In today's data-driven world, our online activities generate a constant stream of personal data. The continuous collection and processing of this data by online services, while often providing convenience, can pose significant risks to individual privacy. Recent privacy regulations such as the GDPR and the CCPA aim to safeguard user privacy by providing users with greater control over their data, but their effective implementation faces numerous challenges. Online services frequently employ manipulative tactics, complex interfaces, and opaque data practices, which hinder users' ability to make informed privacy choices and exercise their rights.In this thesis, we have identified three main gaps in privacy technologies that impact various actors in the privacy ecosystem: users, developers, and regulators.- Lack of Transparency and Accountability (Regulators and Users): The complexity and unreadability of privacy policies prompted regulations like the GDPR to mandate transparency in privacy practices disclosure. However, the actual impact of such regulations on privacy practices remains under-explored. The lack of scalable tools to assess and enforce compliance further hinders the realization of the regulations' intended benefits, leaving a gap in understanding their true effectiveness.- Limited Understanding of Developers' Challenges (Developers): Application developers are required to disclose their privacy practices on app platforms. While there is a growing body of research discussing users' perspectives on privacy disclosures, existing research offers limited insight into the challenges faced by developers in adapting to and effectively utilizing the tools and frameworks provided by app platforms to communicate their privacy practices.- Lack of Usability for Privacy Controls (Users): Existing privacy control interfaces like privacy settings and cookie notices often have complex interfaces employing deceptive patterns. The controls are hard to find, and have usability issues, making it difficult for users to navigate the settings and make informed choices.We address these gaps by adopting a two-pronged approach, aiming to assist all three actors in the privacy ecosystem. First, we develop and evaluate automated tools, powered by Machine Learning (ML) and Natural Language Processing (NLP) techniques, to provide users with control over their privacy and cookie settings. Second, we conduct large-scale studies to assess the landscape of privacy practice disclosure, build automated tools for compliance evaluations to assist regulators and understand developers' perspective as they interact with disclosure mechanisms. Specifically, we make the following contributions:- Developing Automation for Privacy Controls (Users): We develop two novel tools to enable users to get control over their privacy choices:- PriSEC, a Privacy Settings Enforcement Controller that leverages machine learning to enable users to proactively discover and enforce their privacy preferences across websites, reducing the burden on users and promoting proactive privacy management.- CookieEnforcer, a novel system that combines NLP and machine learning to tackle the challenge of deceptive and complex cookie notices. It automatically extracts and executes privacy-preserving cookie choices, simplifying the user experience and reducing the user effort. - Understanding Developers' Perspective (Developers): We investigate the challenges faced by developers in adapting to and utilizing privacy disclosure mechanisms. We employ qualitative analysis methods to examine how privacy practices are reported and the factors influencing these reporting decisions. These insights offer valuable guidance for improving the design and implementation of privacy communication tools.- Assessing Transparency and Accountability (Regulators): We leverage NLP techniques to conduct a comprehensive longitudinal analysis of the impact of the GDPR on online privacy policies. We uncover both positive changes and persistent challenges. We also develop an automated compliance analysis framework. This research helps regulators understand the effectiveness of their efforts and evaluate the transparency of online services.One core objective of this thesis is laying the groundwork for a more privacy-conscious digital landscape, where users are empowered with transparent information and effective tools to exercise meaningful control over their online data; the developers have the necessary tools to effectively disclose their data practices and the regulators have the tools to evaluate the disclosure of privacy practices. We also envision our automated solutions, built upon advances in ML and NLP, being used as the building blocks for a more user-friendly solution for privacy control.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798384059516Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

AutomationIndex Terms--Genre/Form:

554714
Electronic books.

Assessing and Enhancing Users' Online Data Privacy.
LDR:06166ntm a22004097 4500 001 1152527
005 20241203090622.5
006 m o d
007 cr mn ---uuuuu
008 250605s2024 xx obm 000 0 eng d
020 $a 9798384059516
035 $a (MiAaPQ)AAI31560397
035 $a AAI31560397
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Khandelwal, Rishabh. $3 1479545
245 1 0 $a Assessing and Enhancing Users' Online Data Privacy.
264 0 $c 2024
300 $a 1 online resource (236 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: 86-03, Section: B.
500 $a Advisor: Fawaz, Kassem.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2024.
504 $a Includes bibliographical references
520 $a In today's data-driven world, our online activities generate a constant stream of personal data. The continuous collection and processing of this data by online services, while often providing convenience, can pose significant risks to individual privacy. Recent privacy regulations such as the GDPR and the CCPA aim to safeguard user privacy by providing users with greater control over their data, but their effective implementation faces numerous challenges. Online services frequently employ manipulative tactics, complex interfaces, and opaque data practices, which hinder users' ability to make informed privacy choices and exercise their rights.In this thesis, we have identified three main gaps in privacy technologies that impact various actors in the privacy ecosystem: users, developers, and regulators.- Lack of Transparency and Accountability (Regulators and Users): The complexity and unreadability of privacy policies prompted regulations like the GDPR to mandate transparency in privacy practices disclosure. However, the actual impact of such regulations on privacy practices remains under-explored. The lack of scalable tools to assess and enforce compliance further hinders the realization of the regulations' intended benefits, leaving a gap in understanding their true effectiveness.- Limited Understanding of Developers' Challenges (Developers): Application developers are required to disclose their privacy practices on app platforms. While there is a growing body of research discussing users' perspectives on privacy disclosures, existing research offers limited insight into the challenges faced by developers in adapting to and effectively utilizing the tools and frameworks provided by app platforms to communicate their privacy practices.- Lack of Usability for Privacy Controls (Users): Existing privacy control interfaces like privacy settings and cookie notices often have complex interfaces employing deceptive patterns. The controls are hard to find, and have usability issues, making it difficult for users to navigate the settings and make informed choices.We address these gaps by adopting a two-pronged approach, aiming to assist all three actors in the privacy ecosystem. First, we develop and evaluate automated tools, powered by Machine Learning (ML) and Natural Language Processing (NLP) techniques, to provide users with control over their privacy and cookie settings. Second, we conduct large-scale studies to assess the landscape of privacy practice disclosure, build automated tools for compliance evaluations to assist regulators and understand developers' perspective as they interact with disclosure mechanisms. Specifically, we make the following contributions:- Developing Automation for Privacy Controls (Users): We develop two novel tools to enable users to get control over their privacy choices:- PriSEC, a Privacy Settings Enforcement Controller that leverages machine learning to enable users to proactively discover and enforce their privacy preferences across websites, reducing the burden on users and promoting proactive privacy management.- CookieEnforcer, a novel system that combines NLP and machine learning to tackle the challenge of deceptive and complex cookie notices. It automatically extracts and executes privacy-preserving cookie choices, simplifying the user experience and reducing the user effort. - Understanding Developers' Perspective (Developers): We investigate the challenges faced by developers in adapting to and utilizing privacy disclosure mechanisms. We employ qualitative analysis methods to examine how privacy practices are reported and the factors influencing these reporting decisions. These insights offer valuable guidance for improving the design and implementation of privacy communication tools.- Assessing Transparency and Accountability (Regulators): We leverage NLP techniques to conduct a comprehensive longitudinal analysis of the impact of the GDPR on online privacy policies. We uncover both positive changes and persistent challenges. We also develop an automated compliance analysis framework. This research helps regulators understand the effectiveness of their efforts and evaluate the transparency of online services.One core objective of this thesis is laying the groundwork for a more privacy-conscious digital landscape, where users are empowered with transparent information and effective tools to exercise meaningful control over their online data; the developers have the necessary tools to effectively disclose their data practices and the regulators have the tools to evaluate the disclosure of privacy practices. We also envision our automated solutions, built upon advances in ML and NLP, being used as the building blocks for a more user-friendly solution for privacy control.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
650 4 $a Information technology. $3 559429
650 4 $a Computer engineering. $3 569006
650 4 $a Computer science. $3 573171
653 $a Automation
653 $a Transparency
653 $a Usable privacy
653 $a User control
653 $a Privacy practices
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0489
690 $a 0544
690 $a 0464
710 2 $a The University of Wisconsin - Madison. $b Computer Sciences. $3 1179878
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Dissertations Abstracts International $g 86-03B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31560397 $z click for full text (PQDT)