arXiv subjects

Eleanor Birrell

Publications and source records attributed to Eleanor Birrell.

9 recordsLinked to original sources

arXiv2023

SoK: Technical Implementation and Human Impact of Internet Privacy Regulations

Growing recognition of the potential for exploitation of personal data and of the shortcomings of prior privacy regimes has led to the passage of a multitude of new online privacy regulations. Some of these laws -- notably the European Union's General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) -- have been the focus of large bodies of research by the computer science community, while others have received less attention. In this work, we analyze a set of Internet privacy and data protection regulations drawn from around the world -- both those that have frequently been studied by computer scientists and those that have not -- and develop a taxonomy of rights granted and obligations imposed by these laws. We then leverage this taxonomy to systematize 270 technical research papers published in computer science venues that investigate the impact of these laws and explore how technical solutions can complement legal protections. Finally, we analyze the results in this space through an interdisciplinary lens and make recommendations for future work at the intersection of computer science and legal privacy.

cs.CY↗

arXiv2023

Two Steps Forward and One Step Back: The Right to Opt-out of Sale under CPRA

The California Privacy Rights Act (CPRA) was a ballot initiative that revised the California Consumer Privacy Act (CCPA). Although often framed as expanding and enhancing privacy rights, a close analysis of textual revisions -- both changes from the earlier law and changes from earlier drafts of the CPRA guidelines -- suggest that the reality might be more nuanced. In this work, we identify three textual revisions that have potential to negatively impact the right to opt-out of sale under CPRA and evaluate the effect of these textual revisions using (1) a large-scale longitudinal measurement study of 25,000 websites over twelve months and (2) an experimental user study with 775 participants recruited through Prolific. We find that all revisions negatively impacted the usability, scope, and visibility of the right to opt-out of sale. Our results provide the first comprehensive evaluation of the impact of CPRA on Internet privacy. They also emphasize the importance of continued evaluation of legal requirements as guidelines and case law evolve after a law goes into effect.

cs.CY↗

arXiv2023

Data Safety vs. App Privacy: Comparing the Usability of Android and iOS Privacy Labels

Privacy labels -- standardized, compact representations of data collection and data use practices -- are often presented as a solution to the shortcomings of privacy policies. Apple introduced mandatory privacy labels for apps in its App Store in December 2020; Google introduced mandatory labels for Android apps in July 2022. iOS app privacy labels have been evaluated and critiqued in prior work. In this work, we evaluated Android Data Safety Labels and explored how differences between the two label designs impact user comprehension and label utility. We conducted a between-subjects, semi-structured interview study with 12 Android users and 12 iOS users. While some users found Android Data Safety Labels informative and helpful, other users found them too vague. Compared to iOS App Privacy Labels, Android users found the distinction between data collection groups more intuitive and found explicit inclusion of omitted data collection groups more salient. However, some users expressed skepticism regarding elided information about collected data type categories. Most users missed critical information due to not expanding the accordion interface, and they were surprised by collection practices excluded from Android's definitions. Our findings also revealed that Android users generally appreciated information about security practices included in the labels, and iOS users wanted that information added.

cs.HC↗

arXiv2022

Buying Privacy: User Perceptions of Privacy Threats from Mobile Apps

As technology and technology companies have grown in power, ubiquity, and societal influence, some companies -- and notably some mobile apps -- have come to be perceived as privacy threats. Prior work has considered how various factors impact perceptions of threat, including social factors, political speech, and user-interface design. In this work, we investigate how user-visible context clues impact perceptions about whether a mobile application application poses a privacy threat. We conduct a user study with 2109 users in which we find that users depend on context clues -- such as presence of advertising and occurrence (and timing of payment) -- to determine the extent to which a mobile app poses a privacy threat. We also quantify how accurately user assessments match published data collection practices, and we identify a commonly-held misconception about how payments are processed. This work provides new insight into how users assess the privacy threat posed by mobile apps and into social norms around data collection.

cs.HC↗

arXiv2022

The Impact of Visibility on the Right to Opt-out of Sale under CCPA

The California Consumer Protection Act (CCPA) gives users the right to opt-out of sale of their personal information, but prior work has found that opt-out mechanisms provided under this law result in very low opt-out rates. Privacy signals offer a solution for users who are willing to proactively take steps to enable privacy-enhancing tools, but many users are not aware of their rights under CCPA. We therefore explore an alternative approach to enhancing privacy under CCPA: increasing the visibility of opt-out of sale mechanisms. We conduct an user study with 54 participants and find that visible, standardized banners significantly increase opt-out of sale rates in the wild. Participants also report less difficulty opting out and more satisfaction with opt-out mechanisms compared to the native mechanisms currently provided by websites. Our results suggest that effective privacy regulation depends on imposing clear, enforceable visibility standards, and that CCPA's requirements for opt-out of sale mechanisms fall short.

cs.CY↗

arXiv2022

How Well Do My Results Generalize Now? The External Validity of Online Privacy and Security Surveys

Privacy and security researchers often rely on data collected through online crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) and Prolific. Prior work -- which used data collected in the United States between 2013 and 2017 -- found that MTurk responses regarding security and privacy were generally representative for people under 50 or with some college education. However, the landscape of online crowdsourcing has changed significantly over the last five years, with the rise of Prolific as a major platform and the increasing presence of bots. This work attempts to replicate the prior results about the external validity of online privacy and security surveys. We conduct an online survey on MTurk (n=800), a gender-balanced survey on Prolific (n=800), and a representative survey on Prolific (n=800) and compare the responses to a probabilistic survey conducted by the Pew Research Center (n=4272). We find that MTurk response quality has degraded over the last five years, and our results do not replicate the earlier finding about the generalizability of MTurk responses. By contrast, we find that data collected through Prolific is generally representative for questions about user perceptions and experiences, but not for questions about security and privacy knowledge. We also evaluate the impact of Prolific settings, attention check questions, and statistical methods on the external validity of online surveys, and we develop recommendations about best practices for conducting online privacy and security surveys.

cs.HC↗

arXiv2022

Prospects for Improving Password Selection

User-chosen passwords remain essential to online security, and yet people continue to choose weak, insecure passwords. In this work, we investigate whether prospect theory, a behavioral model of how people evaluate risk, can provide insights into how users choose passwords and whether it can motivate new designs for password selection mechanisms that will nudge users to select stronger passwords. We ran a user study with 762 participants, and we found that an intervention guided by prospect theory -- which leverages the reference-dependence effect by framing selecting weak passwords as a loss relative to choosing a stronger password -- causes approximately 25% of users to improve the strength of their password (significantly more than alternative interventions) and reduced the final number of weak passwords by approximately 25%. We also evaluate the relation between user behavior and users' mental models of hacking and password attacks. These results provide guidance for designing and implementing account registration mechanisms that will significantly improve the strength of user-selected passwords, thereby leveraging insights from prospect theory to improve the security of systems that use password-based authentication.

cs.CR↗

arXiv2021

(Un)clear and (In)conspicuous: The right to opt-out of sale under CCPA

The California Consumer Privacy Act (CCPA) -- which began enforcement on July 1, 2020 -- grants California users the affirmative right to opt-out of the sale of their personal information. In this work, we perform a series of observational studies to understand how websites implement this right. We perform two manual analyses of the top 500 U.S. websites (one conducted in July 2020 and a second conducted in January 2021) and classify how each site implements this new requirement. We also perform an automated analysis of the Top 5000 U.S. websites. We find that the vast majority of sites that implement opt-out mechanisms do so with a Do Not Sell link rather than with a privacy banner, and that many of the linked opt-out controls exhibit features such as nudging and indirect mechanisms (e.g., fillable forms). We then perform a pair of user studies with 4357 unique users (recruited from Google Ads and Amazon Mechanical Turk) in which we observe how users interact with different opt-out mechanisms and evaluate how the implementation choices we observed -- exclusive use of links, prevalent nudging, and indirect mechanisms -- affect the rate at which users exercise their right to opt-out of sale. We find that these design elements significantly deter interactions with opt-out mechanisms -- including reducing the opt-out rate for users who are uncomfortable with the sale of their information -- and that they reduce users' awareness of their ability to opt-out. Our results demonstrate the importance of regulations that provide clear implementation requirements in order empower users to exercise their privacy rights.

cs.CR↗

arXiv2021

Policy-Based Federated Learning

In this paper we present PoliFL, a decentralized, edge-based framework that supports heterogeneous privacy policies for federated learning. We evaluate our system on three use cases that train models with sensitive user data collected by mobile phones - predictive text, image classification, and notification engagement prediction - on a Raspberry Pi edge device. We find that PoliFL is able to perform accurate model training and inference within reasonable resource and time budgets while also enforcing heterogeneous privacy policies.

cs.CR↗