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Assessing and Enhancing Users' Online Data Privacy.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Assessing and Enhancing Users' Online Data Privacy./
Author:	Khandelwal, Rishabh.
Description:	1 online resource (236 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
Subject:	Computer science. -
Online resource:	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:

573171
Computer science.
Subjects--Index Terms:

AutomationIndex Terms--Genre/Form:

554714
Electronic books.

Assessing and Enhancing Users' Online Data Privacy.
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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.
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