Maritta Heisel’s research while affiliated with University of Duisburg-Essen and other places

Privacy policies are often used to fulfill the requirement of transparency of data protection legislation like the General Data Protection Regulation of the European Union. The privacy policies are used to describe how the data subject’s data are handled by the data controller. Domain and legal experts mostly create these policies manually. We propose a tool-supported method to improve the creation of accurate privacy policies based on information from the development phase of a system. During privacy and security threat analyses information about system behavior is collected in form of data-flow diagrams. These diagrams describe which data flows from where to where within the system and to which external actors. Based on this data-flow information we can create the basic structure of a privacy policy, already containing the data-flows. The extracted information is one of the most important parts of a privacy policy, providing transparency when data is transferred to external parties.

Stack Overflow (SO) is a popular platform among developers seeking advice on various software-related topics, including privacy and security. As for many knowledge-sharing websites, the value of SO depends largely on users’ engagement, namely their willingness to answer, comment or post technical questions. Still, many of these questions (including cybersecurity-related ones) remain unanswered, putting the site’s relevance and reputation into jeopardy. Hence, it is important to understand users’ participation in privacy and security discussions to promote engagement and foster the exchange of such expertise. Objective: Based on prior findings on online social networks, this work elaborates on the interplay between users’ engagement and their privacy practices in SO. Particularly, it analyses developers’ self-disclosure behaviour regarding profile visibility and their involvement in discussions related to privacy and security. Method: We followed a mixed-methods approach by (i) analysing SO data from 1239 cybersecurity-tagged questions along with 7048 user profiles, and (ii) conducting an anonymous online survey (N=64). Results: About 33% of the questions we retrieved had no answer, whereas more than 50% had no accepted answer. We observed that proactive users tend to disclose significantly less information in their profiles than reactive and unengaged ones. However, no correlations were found between these engagement categories and privacy-related constructs such as perceived control or general privacy concerns. Implications: These findings contribute to (i) a better understanding of developers’ engagement towards privacy and security topics, and (ii) to shape strategies promoting the exchange of cybersecurity expertise in SO.

In a previous publication, we have introduced Risk Issue Questionnaires (RIQs) that serve to support risk identification for critical systems. The starting point of our risk identification method are architectural patterns contained in a system architecture, e.g., process control loops or interactive systems. A RIQ enumerates the typical risks associated with such a pattern. By assessing for each issue contained in a RIQ whether it is relevant or not, risks for the system under analysis are identified in a systematic way.In this paper, we complement the RIQ method by a method to set up and validate CORAS threat models for documenting the identified risks. In this way, we provide a basis to perform the further steps of a model-based risk management process. We equip our RIQs with modeling hints that specify what kind of modeling element should be used to represent a given issue in a threat model. Furthermore, we define formal validation conditions (VCs) that allow the risk modeler to check the generated threat models for coherence and completeness, and present a modeling tool that is able to check the defined VCs.

The General Data Protection Regulation (GDPR) makes the creation of compliant privacy policies a complex process. Our goal is to support policy authors during the creation of privacy policies, by providing them feedback on the privacy policy they are creating. We present the Privacy Policy Compliance Guidance (PriPoCoG) framework supporting policy authors as well as data protection authorities in checking the compliance of privacy policies. To this end we formalize the Layered Privacy Language (LPL) and parts of the GDPR using Prolog. Our formalization, ‘Prolog-LPL’ (P-LPL), points out inconsistencies in a privacy policy and problematic parts of a policy regarding GDPR-compliance. To evaluate P-LPL we translate the Amazon.de privacy policy into P-LPL and perform a compliance analysis on this policy.KeywordsPrivacy policyPolicy languageGeneral Data Protection RegulationFormalizationProlog

Online self-disclosure is perhaps one of the last decade’s most studied communication processes, thanks to the introduction of Online Social Networks (OSNs) like Facebook. Self-disclosure research has contributed significantly to the design of preventative nudges seeking to support and guide users when revealing private information in OSNs. Still, assessing the effectiveness of these solutions is often challenging since changing or modifying the choice architecture of OSN platforms is practically unfeasible. In turn, the effectiveness of numerous nudging designs is supported primarily by self-reported data instead of actual behavioral information. Objective: This work presents ENAGRAM, an app for evaluating preventative nudges, and reports the first results of an empirical study conducted with it. Such a study aims to showcase how the app (and the data collected with it) can be leveraged to assess the effectiveness of a particular nudging approach. Method: We used ENAGRAM as a vehicle to test a risk-based strategy for nudging the self-disclosure decisions of Instagram users. For this, we created two variations of the same nudge (i.e., with and without risk information) and tested it in a between-subjects experimental setting. Study participants (N=22) were recruited via Prolific and asked to use the app regularly for 7 days. An online survey was distributed at the end of the experiment to measure some privacy-related constructs. Results: From the data collected with ENAGRAM, we observed lower (though non-significant) self-disclosure levels when applying risk-based interventions. The constructs measured with the survey were not significant either, except for participants’ External Information Privacy Concerns (EIPC). Implications: Our results suggest that (i) ENAGRAM is a suitable alternative for conducting longitudinal experiments in a privacy-friendly way, and (ii) it provides a flexible framework for the evaluation of a broad spectrum of nudging solutions.

Online self-disclosure is perhaps one of the last decade's most studied communication processes, thanks to the introduction of Online Social Networks (OSNs) like Facebook. Self-disclosure research has contributed significantly to the design of preventative nudges seeking to support and guide users when revealing private information in OSNs. Still, assessing the effectiveness of these solutions is often challenging since changing or modifying the choice architecture of OSN platforms is practically unfeasible. In turn, the effectiveness of numerous nudging designs is supported primarily by self-reported data instead of actual behavioral information. This work presents ENAGRAM, an app for evaluating preventative nudges, and reports the first results of an empirical study conducted with it. Such a study aims to showcase how the app (and the data collected with it) can be leveraged to assess the effectiveness of a particular nudging approach. We used ENAGRAM as a vehicle to test a risk-based strategy for nudging the self-disclosure decisions of Instagram users. For this, we created two variations of the same nudge and tested it in a between-subjects experimental setting. Study participants (N=22) were recruited via Prolific and asked to use the app regularly for 7 days. An online survey was distributed at the end of the experiment to measure some privacy-related constructs. From the data collected with ENAGRAM, we observed lower (though non-significant) self-disclosure levels when applying risk-based interventions. The constructs measured with the survey were not significant either, except for participants' External Information Privacy Concerns. Our results suggest that (i) ENAGRAM is a suitable alternative for conducting longitudinal experiments in a privacy-friendly way, and (ii) it provides a flexible framework for the evaluation of a broad spectrum of nudging solutions.