While protecting user data is essential, software developers often fail to fulfill privacy requirements. However, the reasons why they struggle with privacy-compliant implementation remain unclear. Is it due to a lack of knowledge, or is it because of insufficient support? To provide foundational insights in this field, we conducted a qualitative 5-hour programming study with 30 professional software developers implementing 3 privacy-sensitive programming tasks that were designed with GDPR compliance in mind. To explore if and how developers implement privacy requirements, participants were divided into 3 groups: control, privacy prompted, and privacy expert-supported. After task completion, we conducted follow-up interviews. Alarmingly, almost all participants submitted non-GDPR-compliant solutions (79/90). In particular, none of the 3 tasks were solved privacy-compliant by all 30 participants, with the non-prompted group having the lowest number of 3 out of 30 privacy-compliant solution attempts. Privacy prompting and expert support only slightly improved participants' submissions, with 6/30 and 8/30 privacy-compliant attempts, respectively. In fact, all participants reported severe issues addressing common privacy requirements such as purpose limitation, user consent, or data minimization. Counterintuitively, although most developers exhibited minimal confidence in their solutions, they rarely sought online assistance or contacted the privacy expert, with only 4 out of 10 expert-supported participants explicitly asking for compliance confirmation. Instead, participants often relied on existing implementations and focused on implementing functionality and security first.

Privacy policies are the main mechanism for websites to describe their practices in collecting and processing visitors' personal data. Their format and content are subject to legal requirements that have changed due to recent new privacy regulations including the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and California Privacy Rights Act (CPRA). Studying how privacy policies are adapted to such regulatory change can help identify shortcomings in implementing the law and inform future legislatory initiatives. Existing work in this area mostly studied effects of the GDPR on privacy policies or the "Do Not Sell My Personal Information" link mandated by the CCPA. Methodologically, insights were mainly drawn from English-language privacy policies using keyword-based analyses or machine learning classifiers. In this work, we address this research gap and conduct a bilingual study of privacy policies in English and German that investigates the effects of the GDPR and CCPA/CPRA on privacy policy content, using established methods from corpus linguistics that are language-independent and do not rely on keyword lists or classifiers that may date quickly. We find that, unlike for the GDPR, the CCPA's requirements were not yet widely implemented when it first became enforceable but only with its amendment, the CPRA. Before that, websites used more than 60 variants of the "Do Not Sell" link instead of the mandated wording and did not prominently reference individual rights granted by the CCPA/CPRA. While companies outside California and the US did adapt their disclosures to the CCPA/CPRA, this was limited to English-language policies and did not spill over to policies in German. For GDPR enforcement, we find websites to increasingly rely on legitimate interests to justify data collection, raising concerns whether individuals' interests in the privacy of their personal information are still sufficiently considered.

The COVID-19 pandemic has prompted countries around the world to introduce smartphone apps to support disease control efforts. Their purposes range from digital contact tracing to quarantine enforcement to vaccination passports, and their effectiveness often depends on widespread adoption. While previous work has identified factors that promote or hinder adoption, it has typically examined data collected at a single point in time or focused exclusively on digital contact tracing apps. In this work, we conduct the first representative study that examines changes in people's attitudes towards COVID-19-related smartphone apps for five different purposes over the first 1.5 years of the pandemic. In three survey rounds conducted between Summer 2020 and Summer 2021 in the United States and Germany, with approximately 1,000 participants per round and country, we investigate people's willingness to use such apps, their perceived utility, and people's attitudes towards them in different stages of the pandemic. Our results indicate that privacy is a consistent concern for participants, even in a public health crisis, and the collection of identity-related data significantly decreases acceptance of COVID-19 apps. Trust in authorities is essential to increase confidence in government-backed apps and foster citizens' willingness to contribute to crisis management. There is a need for continuous communication with app users to emphasize the benefits of health crisis apps both for individuals and society, thus counteracting decreasing willingness to use them and perceived usefulness as the pandemic evolves.

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.

Over the last decade, web security research has used notification campaigns as a tool to help web operators fix security problems or stop infrastructure abuse. First attempts at applying this approach to privacy issues focused on single services or vendors. Hence, little is known if notifications can also raise awareness and encourage remediation of more complex, vendor-independent violations of privacy legislation at scale, such as informed consent to cookie usage under the EU's ePrivacy Directive or the General Data Protection Regulation's requirement for a privacy policy. It is also unclear how privacy notifications perform and are perceived compared to those about security vulnerabilities. To fill this research gap, we conduct a large-scale, automated email notification study with more than 115K websites we notify about lack of a privacy policy, use of third-party cookies without or before informed consent, and input forms for personal data that do not use HTTPS. We investigate the impact of warnings about fines and compare the results with security notifications to more than 40K domains about openly accessible Git repositories. Based on our measurements and interactions with operators through email and a survey, we find that notifications about privacy issues are not as well received as security notifications. They result in lower fix rates, less incentive to take immediate action, and more negative feedback. Specific reasons include a lack of awareness and knowledge of privacy laws' applicability, difficulties to pinpoint the problem, and limited intrinsic motivation.

Over the past decade, researchers have started to explore the use of NLP to develop tools aimed at helping the public, vendors, and regulators analyze disclosures made in privacy policies. With the introduction of new privacy regulations, the language of privacy policies is also evolving, and disclosures made by the same organization are not always the same in different languages, especially when used to communicate with users who fall under different jurisdictions. This work explores the use of language technologies to capture and analyze these differences at scale. We introduce an annotation scheme designed to capture the nuances of two new landmark privacy regulations, namely the EU’s GDPR and California’s CCPA/CPRA. We then introduce the first bilingual corpus of mobile app privacy policies consisting of 64 privacy policies in English (292K words) and 91 privacy policies in German (478K words), respectively with manual annotations for 8K and 19K fine-grained data practices. The annotations are used to develop computational methods that can automatically extract “disclosures” from privacy policies. Analysis of a subset of 59 “semi-parallel” policies reveals differences that can be attributed to different regulatory regimes, suggesting that systematic analysis of policies using automated language technologies is indeed a worthwhile endeavor.

While GDPR related fines to big companies like Amazon or Google have seen widespread media attention, data protection authorities have issued several hundred more penalties since 2018. This work analyzes 856 fines and their summaries provided by the CMS Law GDPR Enforcement Tracker. We extend the methodology of previous work that evaluated GDPR fines and, in particular, explore the fines in the light of data flows and we perform a detailed categorization. Our analysis shows that it is a combination of technical and organizational issues that are involved when a fine is imposed. Moreover, data protection authorities more often react to data subjects’ complaints when data breaches become public and when health-related data is involved. We further show that the root causes for fined data processing lie in the early data life cycle phases (e.g., data collection). Here, organizational problems are more prevalent (601 fines) than technical issues (314 fines), while technical issues are mentioned more often in later life cycle phases (e.g., retention, access and usage). Especially mistakes in the early phases of the data collection process (e.g., lacking a legal basis) and unauthorized disclosure in later phases are fined. We cluster the most frequent words and analyze relations to understand where data controllers put personal data at risk. The results confirm that access management is a common problem that results in the unintended disclosure of data.

The COVID-19 pandemic has fueled the development of smartphone applications to assist disease management. Many “corona apps” require widespread adoption to be effective, which has sparked public debates about the privacy, security, and societal implications of government-backed health applications. We conducted a representative online study in Germany (n = 1003), the US (n = 1003), and China (n = 1019) to investigate user acceptance of corona apps, using a vignette design based on the contextual integrity framework. We explored apps for contact tracing, symptom checks, quarantine enforcement, health certificates, and mere information. Our results provide insights into data processing practices that foster adoption and reveal significant differences between countries, with user acceptance being highest in China and lowest in the US. Chinese participants prefer the collection of personalized data, while German and US participants favor anonymity. Across countries, contact tracing is viewed more positively than quarantine enforcement, and technical malfunctions negatively impact user acceptance.

Abstract Privacy policies have become a focal point of privacy research. With their goal to reflect the privacy practices of a website, service, or app, they are often the starting point for researchers who analyze the accuracy of claimed data practices, user understanding of practices, or control mechanisms for users. Due to vast differences in structure, presentation, and content, it is often challenging to extract privacy policies from online resources like websites for analysis. In the past, researchers have relied on scrapers tailored to the specific analysis or task, which complicates comparing results across different studies. To unify future research in this field, we developed a toolchain to process website privacy policies and prepare them for research purposes. The core part of this chain is a detector module for English and German, using natural language processing and machine learning to automatically determine whether given texts are privacy or cookie policies. We leverage multiple existing data sets to refine our approach, evaluate it on a recently published longitudinal corpus, and show that it contains a number of misclassified documents. We believe that unifying data preparation for the analysis of privacy policies can help make different studies more comparable and is a step towards more thorough analyses. In addition, we provide insights into common pitfalls that may lead to invalid analyses.

Attackers use various domain squatting techniques to convince users that their services are legitimate. Previous work has shown that methods liketyposquatting, where single characters are removed or duplicated, can successfully deceive users.

The European General Data Protection Regulation (GDPR), which went into effect in May 2018, brought new rules for the processing of personal data that affect many business models, including online advertising. The regulation's definition of personal data applies to every company that collects data from European Internet users. This includes tracking services that, until then, argued that they were collecting anonymous information and data protection requirements would not apply to their businesses. Previous studies have analyzed the impact of the GDPR on the prevalence of online tracking, with mixed results. In this paper, we go beyond the analysis of the number of third parties and focus on the underlying information sharing networks between online advertising companies in terms of client-side cookie syncing. Using graph analysis, our measurement shows that the number of ID syncing connections decreased by around 40% around the time the GDPR went into effect, but a long-term analysis shows a slight rebound since then. While we can show a decrease in information sharing between third parties, which is likely related to the legislation, the data also shows that the amount of tracking, as well as the general structure of cooperation, was not affected. Consolidation in the ecosystem led to a more centralized infrastructure that might actually have negative effects on user privacy, as fewer companies perform tracking on more sites.

In the modern Web, service providers often rely heavily on third parties to run their services. For example, they make use of ad networks to finance their services, externally hosted libraries to develop features quickly, and analytics providers to gain insights into visitor behavior.

Ad personalization has been criticized in the past for invading privacy, lack of transparency, and improper controls offered to users. Recently, companies started to provide web portals and other means for users to access data collected about them. In this paper, we study these new transparency tools from multiple perspectives using a mixed-methods approach. Still practices of data sharing barely changed until recently when new legislation required all companies to grant individual access to personal data stored about them. Using a mixed-methods approach we study the benefits of the new rights for users. First, we analyze transparency tools provided by 22 companies and check whether they follow previous recommendations for usability and user expectations. Based on these insights, we conduct a survey with 490 participants to evaluate three common approaches to disclose data. To complement this user-centric view, we shed light on the design decisions and complexities of transparency in online advertising using an online survey (n = 24) and in-person interviews (n = 8) with experts from the industry. We find that newly created transparency tools present a variety of information to users, from detailed technical logs to high-level interest segment information. Our results indicate that users do not (yet) know what to learn from the data and mistrust the accuracy of the information shown to them. At the same time, new transparency requirements pose several challenges to an industry that excessively shares data that even they sometimes cannot relate to an individual.

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.

As increasingly many Internet-of-Things (IoT) devices collect personal data, users face more privacy decisions. Personal privacy assistants can provide social cues and help users make informed decisions by presenting information about how others have decided in similar cases. To better understand which social cues are relevant and whose recommendations users are more likely to follow, we presented 1000 online participants with nine IoT data-collection scenarios. Some participants were told the percentage of experts or friends who allowed data collection in each scenario, while other participants were provided no social cue. At the conclusion of each scenario, participants were asked whether they would allow the described data collection. Our results help explain under what circumstances users are more or less likely to be swayed by the reported behavior of others in similar scenarios. For example, our results indicate that when friends denied data collection, our participants were more influenced than when friends allowed data collection. On the other hand, participants were more influenced by experts when they allowed data collection. We also observed that influence could get stronger or wear off when participants were exposed to a sequence of scenarios. For example, when experts and friends repeatedly allowed data collection in scenarios with clear risk or denied it in scenarios with clear benefits, participants were less likely to be influenced by them in subsequent scenarios.

Reflection is a common means to improve work: Every day, people think back to past work and - oftentimes in a group - try to find out whether they can improve it or whether they can derive better practices from it. However, especially collaborative reflection is neglected in research and design and consequently, there are hardly any insights on how it takes place in the practice of daily work and how tools can support it. To shed light on these questions, this paper presents a case that has been analyzed in a hospital as part of a series of studies on collaborative reflection in practice. Focusing this case and backing it with the other studies, the paper presents peculiarities and needs of collaborative reflection in healthcare workplaces as well as a more general formalization of collaborative reflection characteristics. Based on these results, an application to support physicians in their reflection was prototyped and tested. The presented results primarily apply to healthcare workplaces, but also cover general findings for the support of collaborative reflection.