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Privacy Impact Assessments (PIAs) offer a process for assessing the privacy impacts of a project or system. As a privacy engineering strategy, they are one of the main approaches to privacy by design, supporting the early identification of threats and controls. However, there is still a shortage of empirical evidence on their use and proven effectiveness in practice. To better understand the current literature and research, this paper provides a comprehensive Scoping Review (ScR) on the topic of PIAs “in the wild,” following the well-established Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. This ScR includes 45 studies, providing an extensive synthesis of the existing body of knowledge, classifying types of research and publications, appraising the methodological quality of primary research, and summarising the positive and negative aspects of PIAs in practice, as reported by those studies. This ScR also identifies significant research gaps (e.g., evidence gaps from contradictory results and methodological gaps from research design deficiencies), future research pathways, and implications for researchers, practitioners, and policymakers developing and using PIA frameworks. As we conclude, there is still a significant need for more primary research on the topic, both qualitative and quantitative. A critical appraisal of qualitative studies revealed deficiencies in the methodological quality, and only four quantitative studies were identified, suggesting that current primary research remains incipient. Nonetheless, PIAs can be regarded as a prominent sub-area in the broader field of empirical privacy engineering, in which further scientific research to support existing practices is needed., Digital Health Innovation (DHINO) Project, DigitalWell Arena Project

Every Web session involves a DNS resolution. While, in the last decade, we witnessed a promising trend towards an encrypted Web in general, DNS encryption has only recently gained traction with the standardisation of DNS over TLS (DoT) and DNS over HTTPS (DoH). Meanwhile, the rapid rise of QUIC deployment has now opened up an exciting opportunity to utilise the same protocol to not only encrypt Web communications, but also DNS. In this paper, we evaluate this benefit of using QUIC to coalesce name resolution via DNS over QUIC (DoQ), and Web content delivery via HTTP/3 (H3) with 0-RTT. We compare this scenario using several possible combinations where H3 is used in conjunction with DoH and DoQ, as well as the unencrypted DNS over UDP (DoUDP). We observe, that when using H3 1-RTT, page load times with DoH can get inflated by >30% over fixed-line and by >50% over mobile when compared to unencrypted DNS with DoUDP. However, this cost of encryption can be drastically reduced when encrypted connections are coalesced (DoQ + H3 0-RTT), thereby reducing the page load times by 1/3 over fixed-line and 1/2 over mobile, overall making connection coalescing with QUIC the best option for encrypted communication on the Internet.

Intelligent Transportation Systems (ITS) are on the rise, yet the knowledge about privacy preferences by different types of drivers in this context needs to be improved. This paper presents survey-based research (N = 528) focusing on preferences of drivers from South Africa and the Nordic countries for data processing and sharing by ITS, including future vehicular ad hoc networks. Our results indicate regionally framed drivers’ privacy attitudes and behaviours. South African participants have higher privacy concerns and risk perception. However, their preferences to share location data with police, family and friends, emergency services, and insurance companies are higher. Moreover, the region significantly affects preferences for transparency and control and sharing frequency, as well as willingness to pay for privacy, which are higher among the South Africans. We discuss how our results on factors, including region, impacting drivers’ privacy preferences can contribute to the design of usable privacy and identity management for ITS.

Privacy Impact Assessments (PIAs) offer a systematic process for assessing the privacy impacts of a project or system. As a privacy engineering strategy, PIAs are heralded as one of the main approaches to privacy by design, supporting the early identification of threats and controls. However, there is still a shortage of empirical evidence on their uptake and proven effectiveness in practice. To better understand the current state of literature and research, this paper provides a comprehensive Scoping Review (ScR) on the topic of PIAs "in the wild", following the well-established Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. As a result, this ScR includes 45 studies, providing an extensive synthesis of the existing body of knowledge, classifying types of research and publications, appraising the methodological quality of primary research, and summarising the positive and negative aspects of PIAs in practice, as reported by studies. This ScR also identifies significant research gaps (e.g., evidence gaps from contradictory results and methodological gaps from research design deficiencies), future research pathways, and implications for researchers, practitioners, and policymakers developing and evaluating PIA frameworks. As we conclude, there is still a significant need for more primary research on the topic, both qualitative and quantitative. A critical appraisal of qualitative studies (n=28) revealed deficiencies in the methodological quality, and only four quantitative studies were identified, suggesting that current primary research remains incipient. Nonetheless, PIAs can be regarded as a prominent sub-area in the broader field of Empirical Privacy Engineering, warranting further research toward more evidence-based practices., Comment: 65 pages, 9 figures

This chapter presents two contributions; the first is a method and the second is the results of applying this method to usable privacy. First, we introduce a general method for validating ideas based on expert opinions. We adapt techniques that normally are used for validating data and apply them instead to analyze the expert opinions on the ideas under study. Since usually the expert opinions are varied, example-rich, and forward-looking, applying our method of ideas validation has the side effect of also identifying, in the process, open problems where the original studied ideas function as a foundation for further developments. Second, we employ a critical qualitative research, using theory triangulation to analyze the opinions coming from three groups of experts, categorized as “certifications,” “law,” and “usability.” These took part in a study where we thoroughly applied the method that we present here in order to validate five different types of ideas previously published under the collective title “Making GDPR Usable.” We will thus show how to validate ideas that come in the form of: a model, a definition, a prescriptive list, a set of criteria, and a form of rather general research idea as those usually appearing in position papers, namely “the need for evaluations and measuring of usability of privacy.”

Privacy enhancing technologies allow the minimization of risks to online data. However, the transparency of the minimization process is not so clear to all types of end users. Privacy Impact Assessments (PIAs) is a standardized tool that identifies and assesses privacy risks associated with the use of a system. In this work, we used the results of the PIA conducted in our use case to visualize privacy risks to end users in the form of User Interface (UI) mock ups. We tested and evaluated the UI mock-ups via walkthroughs to investigate users' interests by observing their clicking behavior, followed by four focus group workshops. There were 13 participants (two expert groups and two lay user groups) in total. Results reveal general interests in the transparency provided by showing the risks reductions. Generally, although participants appreciate the concept of having detailed information provided about risk reductions and the type of risks, the visualization and usability of the PIA UIs require future development. Specifically, it should be tailored to the target group's mental models and background knowledge.

Usable explanations of privacy-enhancing technologies (PETs) help users make more informed privacy decisions, but the explanations of PETs are generally geared toward individuals with more technical knowledge. To explain functional encryption (FE) to experts and laypersons, we investigate structural and functional explanations and explore users' interests and preferences, as well as how they affect users' comprehension and decisions about sharing data. To this end (with an EU-based population), we conducted four focus groups, in combination with walk-throughs, with 13 participants in the first study, followed by an online survey with 347 experts and 370 laypersons. Both explanations were considered useful in fulfilling the different needs of participants interested in the privacy policy information. Participants, regardless of their expertise, trusted and were more satisfied with the structural explanation. However, functional explanations had a higher contribution to all participants' comprehension. We, therefore, recommend combining both types of explanations for a usable privacy policy.

This report documents the outcomes of Dagstuhl Seminar 22342 “Privacy in Speech and LanguageTechnology”. The seminar brought together 27 attendees from 9 countries (Australia, Belgium,France, Germany, the Netherlands, Norway, Portugal, Sweden, and the USA) and 6 distinctdisciplines (Speech Processing, Natural Language Processing, Privacy Enhancing Technologies,Machine Learning, Human Factors, and Law) in order to achieve a common understanding of theprivacy threats raised by speech and language technology, as well as the existing solutions andthe remaining issues in each discipline, and to draft an interdisciplinary roadmap towards solvingthose issues in the short or medium term.To achieve these goals, the first day and the morning of the second day were devoted to3-minute self-introductions by all participants intertwined with 6 tutorials to introduce theterminology, the problems faced, and the solutions brought in each of the 6 disciplines. We alsomade a list of use cases and identified 6 cross-disciplinary topics to be discussed. The remainingdays involved working groups to discuss these 6 topics, collaborative writing sessions to report onthe findings of the working groups, and wrap-up sessions to discuss these findings with each other.A hike was organized in the afternoon of the third day.The seminar was a success: all participants actively participated in the working groups andthe discussions, and went home with new ideas and new collaborators. This report gathers theabstracts of the 6 tutorials and the reports of the working groups, which we consider as valuablecontributions towards a full-fledged roadmap., Seminar August 21–26, 2022 – https://www.dagstuhl.de/22342

The Internet of Things (IoT) devices are rapidly increasing in popularity, with more individuals using Internet-connected devices that continuously monitor their activities. This work explores privacy concerns and expectations of end-users related to Trigger-Action platforms (TAPs) in the context of the Internet of Things (IoT). TAPs allow users to customize their smart environments by creating rules that trigger actions based on specific events or conditions. As personal data flows between different entities, there is a potential for privacy concerns. In this study, we aimed to identify the privacy factors that impact users’ concerns and preferences for using IoT TAPs. To address this research objective, we conducted three focus groups with 15 participants and we extracted nine themes related to privacy factors using thematic analysis. Our participants particularly prefer to have control and transparency over the automation and are concerned about unexpected data inferences, risks and unforeseen consequences for themselves and for bystanders that are caused by the automation. The identified privacy factors can help researchers derive predefined and selectable profiles of privacy permission settings for IoT TAPs that represent the privacy preferences of different types of users as a basis for designing usable privacy controls for IoT TAPs.

The use of Privacy-Enhancing Technologies in the field of data anonymisation and pseudonymisation raises a lot of questions with respect to legal compliance under GDPR and current international data protection legislation. Here, especially the use of innovative technologies based on machine learning may increase or decrease risks to data protection. A workshop held at the IFIP Summer School on Privacy and Identity Management showed the complexity of this field and the need for further interdisciplinary research on the basis of an improved joint understanding of legal and technical concepts.

Online education including MOOCs (Massive Open Online Courses) have steadily gained importance during the COVID-19 pandemic. They also play an important role for enabling lifelong learning and addressing the cybersecurity skills gap. However, it is not always easy to judge the quality of MOOCs for learners or other stakeholders including organisations interested in cybersecurity MOOCs as a means for competence development of their employees. This article provides an overview of the research work conducted by the EU H2020 projects CyberSec4Europe and CONCORDIA on eliciting quality criteria for different types of cybersecurity MOOCs provided in Europe, on defining a quality branding process that was validated through trial evaluations of selected cybersecurity MOOCs, and on conducting a survey with cybersecurity MOOC stakeholders and interviews with certification experts about the role and form of quality certification. Based on this research, the article concludes by proposing building blocks for a “lightweight” certification scheme for future quality branding of cybersecurity MOOC.

Users are often overwhelmed by privacy decisions to manage their personal data, which can happen on the web, in mobile, and in IoT environments. These decisions can take various forms -such as decisions for setting privacy permissions or privacy preferences, decisions responding to consent requests, or to intervene and ’reject’ processing of one’s personal data -, and each can have different legal impacts. In all cases and for all types of decisions, scholars and industry have been proposing tools to better automate the process of privacy decisions at different levels, in order to enhance usability. We provide in this paper an overview of the main challenges raised by the automation of privacy decisions, together with a classification scheme of the existing and envisioned work and proposals addressing automation of privacy decisions.

Privacy-enhancing technologies (PETs) provide technical building blocks for achieving privacyby design and can be defined as technologies that embody fundamental data protection goals[13 ] including the goals of unlinkability, interveneability, transparency and the classical CIA(confidentiality, integrity, availability) security goals by minimizing personal data collectionand use, maximizing data security, and empowering individuals.The privacy by design principle of a positive sum for speech and language technologiesshould enable users to benefit from the rich functions of these technologies while protectingthe users’ privacy at the same time. The fundamental question is how to achieve privacyby design for speech and language technology without hampering the services. To achievethis goal, different PETs exist that can be utilized for this purpose. Below, we first discusswhat type of personal data are accessible via speech and text and should be the target ofprotection by PETs. Then, we provide an overview of PETs that can provide protectionand discuss their limitations and challenges that arise when used for speech and languagetechnologies.

This report documents the outcomes of Dagstuhl Seminar 22342 "Privacy in Speech and Language Technology". The seminar brought together 27 attendees from 9 countries (Australia, Belgium, France, Germany, the Netherlands, Norway, Portugal, Sweden, and the USA) and 6 distinct disciplines (Speech Processing, Natural Language Processing, Privacy Enhancing Technologies, Machine Learning, Human Factors, and Law) in order to achieve a common understanding of the privacy threats raised by speech and language technology, as well as the existing solutions and the remaining issues in each discipline, and to draft an interdisciplinary roadmap towards solving those issues in the short or medium term. To achieve these goals, the first day and the morning of the second day were devoted to 3-minute self-introductions by all participants intertwined with 6 tutorials to introduce the terminology, the problems faced, and the solutions brought in each of the 6 disciplines. We also made a list of use cases and identified 6 cross-disciplinary topics to be discussed. The remaining days involved working groups to discuss these 6 topics, collaborative writing sessions to report on the findings of the working groups, and wrap-up sessions to discuss these findings with each other. A hike was organized in the afternoon of the third day. The seminar was a success: all participants actively participated in the working groups and the discussions, and went home with new ideas and new collaborators. This report gathers the abstracts of the 6 tutorials and the reports of the working groups, which we consider as valuable contributions towards a full-fledged roadmap.