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Florian Mercks:

Delineating Research Communities in Human-Centered Privacy and Security


  • Required:
    • Solid experiences in web development (html/css/javascript, python) and proficiency in English
    • VL Human-Computer Interaction 1 or VL Data Visualization
  • Preferred: Completion of the lecture on "User-Centered Design"/"Human-Computer Interaction I" and the lecture on "Wissenschaftliches Arbeiten in der Informatik" sowie "Empirische Bewertung in der Informatik"
Information extraction, Data visualization, Empirical computer science
Master of Science (M.Sc.)


The importance of privacy and security in the face of new technological possibilities and social change has been and still is a highly debated topic; think of the Corona Tracing App, for example. The fact that individuals are often unaware of their privacy consequences was one of the main incentives for the General Data Protection Regulation, which came into effect within the European Union in 2018. Despite this undertaking to regulate the processing of personal data, privacy remains fundamentally controversial [7]. This thesis aims to identify research communities and perspectives in HCI closely tied to this issue or have remained isolated to date to leverage this essential contestability of privacy productively. Such conceptual analytics allow us as HCI researchers to focus and analyze the multiple uses of privacy in different contexts.

Within the HCI research field, various disconnected communities exist that address different notions of privacy and security. For example, previous research from the AI and ML fields tries to address a similar issue of a scattered research field [1]. Shneiderman et al. discuss the need for HCI research that allow users to "better understand the underlying computational processes" and give users "the potential to better control their (the algorithms') actions" [9]. This defines the core of this thesis by capturing and delineating the existing terminology to highlights the relations and perspectives in varying privacy and security settings (e. g., when donating health data or tracking motion data). For this, a systematic literature, which is widely used in various areas (e.g., empowerment [8], social acceptability [4], software visualization [6]) is used, to get an overview over the field.

In the first step, the diversity of the different perspectives need to be captured by specifying a reproducible study design [3]. For this, a literature study will be conducted to employ hierarchical clustering and network analysis (e. g., [2, 5]). With a focus on the HCI community (CHI conference and SIGCHI specialized conferences), such systematic literature analysis can be realized from a methodological perspective by a "recursive query design" [10]. Such analysis enables a critical research approach and an interdisciplinary overview to draw on research from different areas by visualizing the resulting citation graph to illustrate relationships between keywords and frequently discussed topics. Finally, the diversity of perspectives (also outside the HCI discipline) can be elaborated based on the identified clusters and exemplary research from each cluster needs to be discussed.

Possible Procedure

  1. Specify a reproducible study design (Jupyter Notebook)
  2. Conducting a systematic literature study on privacy and security with HCI focus
  3. Visualize the resulting citation network, for example by using Gephi, identify the different research communities
  4. Elaborate the different research communities and their terminologies and how they relate to each other


[1] Abdul, Ashraf, u. a. „Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda“. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, ACM, 2018, S. 1–18. DOI.org (Crossref), doi:10.1145/3173574.3174156.

[2] Callon, M., Courtial, J.-P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191–235. doi:10.1177/053901883022002003

[3] Kitzes, J., Turek, D., & Deniz, F. (Eds.). (2018). The Practice of Reproducible Research: Case Studies and Lessons from the Data-Intensive Sciences. Oakland, CA: University of California Press. http://www.practicereproducibleresearch.org/

[4] Marion Koelle, Swamy Ananthanarayan, and Susanne Boll. 2020. Social Acceptability in HCI: A Survey of Methods, Measures, and Design Strategies. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI '20). Association for Computing Machinery, New York, NY, USA, 1–19. DOI:https://doi.org/10.1145/3313831.3376162

[5] Liu, Yong, u. a. „CHI 1994-2013: Mapping Two Decades of Intellectual Progress through Co-Word Analysis“. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, 2014, S. 3553–62. DOI.org (Crossref), doi:10.1145/2556288.2556969.

[6] L. Merino, M. Ghafari, C. Anslow and O. Nierstrasz, "A systematic literature review of software visualization evaluation", Journal of Systems and Software, vol. 144, pp. 165-180, 2018.

[7] Mulligan, Deirdre K., u. a. Privacy Is an Essentially Contested Concept: A Multi-Dimensional Analytic for Mapping Privacy. S. 17.

[8] Hanna Schneider, Malin Eiband, Daniel Ullrich, and Andreas Butz. Empowerment in HCI - A Survey and Framework. 2018. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI'18). ACM, NY, NY, 1--14. DOI: http://dx.doi.org/10.1145/3173574.3173818

[9] Shneiderman, Ben & Plaisant, Catherine & Cohen, Maxine & Jacobs, Steven & Elmqvist, Niklas & Diakopoulos, Nicholoas. (2016). Grand challenges for HCI researchers. interactions. 23. 24-25. 10.1145/2977645.

[10] Wieringa, Maranke. „What to Account for When Accounting for Algorithms: A Systematic Literature Review on Algorithmic Accountability“. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, ACM, 2020, S. 1–18. DOI.org (Crossref), doi:10.1145/3351095.3372833.