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Recent advances in large language models have enabled the creation of highly effective chatbots, which may serve as a platform for targeted advertising. This paper investigates the risks of personalizing advertising in chatbots to their users. We developed a chatbot that embeds personalized product advertisements within LLM responses, inspired by similar forays by AI companies. Our benchmarks show that ad injection impacted certain LLM attribute performance, particularly response desirability. We conducted a between-subjects experiment with 179 participants using chabots with no ads, unlabeled targeted ads, and labeled targeted ads. Results revealed that participants struggled to detect chatbot ads and unlabeled advertising chatbot responses were rated higher. Yet, once disclosed, participants found the use of ads embedded in LLM responses to be manipulative, less trustworthy, and intrusive. Participants tried changing their privacy settings via chat interface rather than the disclosure. Our findings highlight ethical issues with integrating advertising into chatbot responses

We draw on the Protection Motivation Theory (PMT) to design nudges that encourage users to change breached passwords. Our online experiment ($n$=$1,386$) compared the effectiveness of a threat appeal (highlighting negative consequences of breached passwords) and a coping appeal (providing instructions on how to change the breached password) in a 2x2 factorial design. Compared to the control condition, participants receiving the threat appeal were more likely to intend to change their passwords, and participants receiving both appeals were more likely to end up changing their passwords; both comparisons have a small effect size. Participants' password change behaviors are further associated with other factors such as their security attitudes (SA-6) and time passed since the breach, suggesting that PMT-based nudges are useful but insufficient to fully motivate users to change their passwords. Our study contributes to PMT's application in security research and provides concrete design implications for improving compromised credential notifications., Comment: Manuscript under review at ACM Transactions on Computer-Human Interaction

Privacy policies are crucial for informing users about data practices, yet their length and complexity often deter users from reading them. In this paper, we propose an automated approach to identify and visualize data practices within privacy policies at different levels of detail. Leveraging crowd-sourced annotations from the ToS;DR platform, we experiment with various methods to match policy excerpts with predefined data practice descriptions. We further conduct a case study to evaluate our approach on a real-world policy, demonstrating its effectiveness in simplifying complex policies. Experiments show that our approach accurately matches data practice descriptions with policy excerpts, facilitating the presentation of simplified privacy information to users.

Modern websites frequently use and embed third-party services to facilitate web development, connect to social media, or for monetization. This often introduces privacy issues as the inclusion of third-party services on a website can allow the third party to collect personal data about the website's visitors. While the prevalence and mechanisms of third-party web tracking have been widely studied, little is known about the decision processes that lead to websites using third-party functionality and whether efforts are being made to protect their visitors' privacy. We report results from an online survey with 395 participants involved in the creation and maintenance of websites. For ten common website functionalities we investigated if privacy has played a role in decisions about how the functionality is integrated, if specific efforts for privacy protection have been made during integration, and to what degree people are aware of data collection through third parties. We find that ease of integration drives third-party adoption but visitor privacy is considered if there are legal requirements or respective guidelines. Awareness of data collection and privacy risks is higher if the collection is directly associated with the purpose for which the third-party service is used., Comment: 25 pages, 8 figures, 7 tables

Since the adoption of the General Data Protection Regulation (GDPR) in May 2018 more than 60 % of popular websites in Europe display cookie consent notices to their visitors. This has quickly led to users becoming fatigued with privacy notifications and contributed to the rise of both browser extensions that block these banners and demands for a solution that bundles consent across multiple websites or in the browser. In this work, we identify common properties of the graphical user interface of consent notices and conduct three experiments with more than 80,000 unique users on a German website to investigate the influence of notice position, type of choice, and content framing on consent. We find that users are more likely to interact with a notice shown in the lower (left) part of the screen. Given a binary choice, more users are willing to accept tracking compared to mechanisms that require them to allow cookie use for each category or company individually. We also show that the wide-spread practice of nudging has a large effect on the choices users make. Our experiments show that seemingly small implementation decisions can substantially impact whether and how people interact with consent notices. Our findings demonstrate the importance for regulation to not just require consent, but also provide clear requirements or guidance for how this consent has to be obtained in order to ensure that users can make free and informed choices., Comment: 18 pages, 6 figures, 2019 ACM SIGSAC Conference on Computer and Communications Security (CCS '19), November 11-15, 2019, London, United Kingdom

The European Union's General Data Protection Regulation (GDPR) went into effect on May 25, 2018. Its privacy regulations apply to any service and company collecting or processing personal data in Europe. Many companies had to adjust their data handling processes, consent forms, and privacy policies to comply with the GDPR's transparency requirements. We monitored this rare event by analyzing the GDPR's impact on popular websites in all 28 member states of the European Union. For each country, we periodically examined its 500 most popular websites - 6,579 in total - for the presence of and updates to their privacy policy. While many websites already had privacy policies, we find that in some countries up to 15.7 % of websites added new privacy policies by May 25, 2018, resulting in 84.5 % of websites having privacy policies. 72.6 % of websites with existing privacy policies updated them close to the date. Most visibly, 62.1 % of websites in Europe now display cookie consent notices, 16 % more than in January 2018. These notices inform users about a site's cookie use and user tracking practices. We categorized all observed cookie consent notices and evaluated 16 common implementations with respect to their technical realization of cookie consent. Our analysis shows that core web security mechanisms such as the same-origin policy pose problems for the implementation of consent according to GDPR rules, and opting out of third-party cookies requires the third party to cooperate. Overall, we conclude that the GDPR is making the web more transparent, but there is still a lack of both functional and usable mechanisms for users to consent to or deny processing of their personal data on the Internet., Comment: Published at NDSS 2019

Privacy policies are the primary channel through which companies inform users about their data collection and sharing practices. These policies are often long and difficult to comprehend. Short notices based on information extracted from privacy policies have been shown to be useful but face a significant scalability hurdle, given the number of policies and their evolution over time. Companies, users, researchers, and regulators still lack usable and scalable tools to cope with the breadth and depth of privacy policies. To address these hurdles, we propose an automated framework for privacy policy analysis (Polisis). It enables scalable, dynamic, and multi-dimensional queries on natural language privacy policies. At the core of Polisis is a privacy-centric language model, built with 130K privacy policies, and a novel hierarchy of neural-network classifiers that accounts for both high-level aspects and fine-grained details of privacy practices. We demonstrate Polisis' modularity and utility with two applications supporting structured and free-form querying. The structured querying application is the automated assignment of privacy icons from privacy policies. With Polisis, we can achieve an accuracy of 88.4% on this task. The second application, PriBot, is the first freeform question-answering system for privacy policies. We show that PriBot can produce a correct answer among its top-3 results for 82% of the test questions. Using an MTurk user study with 700 participants, we show that at least one of PriBot's top-3 answers is relevant to users for 89% of the test questions., Comment: Published at USENIX Security 2018; associated website: https://pribot.org

Data aggregators collect large amount of information about individual users and create detailed online behavioral profiles of individuals. Behavioral profiles benefit users by improving products and services. However, they have also raised concerns regarding user privacy, transparency of collection practices and accuracy of data in the profiles. To improve transparency, some companies are allowing users to access their behavioral profiles. In this work, we investigated behavioral profiles of users by utilizing these access mechanisms. Using in-person interviews (n=8), we analyzed the data shown in the profiles, elicited user concerns, and estimated accuracy of profiles. We confirmed our interview findings via an online survey (n=100). To assess the claim of improving transparency, we compared data shown in profiles with the data that companies have about users. More than 70% of the participants expressed concerns about collection of sensitive data such as credit and health information, level of detail and how their data may be used. We found a large gap between the data shown in profiles and the data possessed by companies. A large number of profiles were inaccurate with as much as 80% inaccuracy. We discuss implications for public policy management., Comment: in Ashwini Rao, Florian Schaub, and Norman Sadeh What do they know about me? Contents and Concerns of Online Behavioral Profiles (2014) ASE BigData/SocialInformatics/PASSAT/BioMedCom Conference