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Data Minimisation Potential for Timestamps in Git: An Empirical Analysis of User Configurations

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ICT Systems Security and Privacy Protection (SEC 2022)


With the increasing digitisation, more and more of our activities leave digital traces. This is especially true for our work life. Data protection regulations demand the consideration of employees’ right to privacy and that the recorded data is necessary and proportionate for the intended purpose. Prior work indicates that standard software commonly used in workplace environments records user activities in excessive detail. A major part of this are timestamps, whose temporal contextualisation facilitates monitoring. Applying data minimisation on timestamps is however dependent on an understanding of their necessity. We provide large-scale real-world evidence of user demand for timestamp precision. We analysed over 20 000 Git configuration files published on GitHub with regard to date-related customisation in output and filtering, and found that a large proportion of users choose customisations with lower or adaptive precision: almost 90% of chosen output formats for subcommand aliases use reduced or adaptive precision and about 75% of date filters use day precision or less. We believe that this is evidence for the viability of timestamp minimisation. We evaluate possible privacy gains and functionality losses and present a tool to reduce Git dates.

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  1. Burkert, C.: gitconfig date study dataset (2022).

  2. Burkert, C., Federrath, H.: Towards minimising timestamp usage in application software. In: Pérez-Solà, C., Navarro-Arribas, G., Biryukov, A., Garcia-Alfaro, J. (eds.) Data Privacy Management, Cryptocurrencies and Blockchain Technology, DPM/CBT -2019. LNCS, vol. 11737, pp. 138–155. Springer, Cham (2019).

  3. Claes, M., Mäntylä, M.V., Kuutila, M., Adams, B.: Do programmers work at night or during the weekend? In: ICSE. ACM (2018)

    Google Scholar 

  4. Drakonakis, K., Ilia, P., Ioannidis, S., Polakis, J.: Please forget where I was last summer: the privacy risks of public location (meta)data. In: NDSS (2019)

    Google Scholar 

  5. EMPRI-DEVOPS: git-privacy.

  6. Eyolfson, J., Tan, L., Lam, P.: Do time of day and developer experience affect commit bugginess? In: MSR. ACM (2011)

    Google Scholar 

  7. Game World Observer: Xsolla cites growth rate slowdown as reason for layoffs, CEO’s tweet causes further controversy (2021).

  8. Git: Git Source Code (2021).

  9. Git: Reference (2022). Accessed 28 March 2022

  10. GitHub Docs: Best practices for integrators (2021). Accessed 24 Sep 2021

  11. GitHub Docs: Search API (2021). search. Accessed 24 Sep 2021

  12. Gousios, G.: The GHTorrent dataset and tool suite. In. MSR 2013 (2013)

    Google Scholar 

  13. Mavriki, P., Karyda, M.: Profiling with big data: identifying privacy implications for individuals, groups and society. In: MCIS (2018)

    Google Scholar 

  14. Senarath, A., Arachchilage, N.A.G.: Understanding software developers’ approach towards implementing data minimization (2018).

  15. Slagell, A.J., Lakkaraju, K., Luo, K.: FLAIM: a multi-level anonymization framework for computer and network logs. In: LISA, pp. 63–77. USENIX (2006)

    Google Scholar 

  16. Traullé, B., Dalle, J.-M.: The evolution of developer work rhythms. In: Staab, S., Koltsova, O., Ignatov, D.I. (eds.) Social Informatics, SocInfo 2018. LNCS, vol. 11185, pp. 420–438. Springer, Cham (2018).

  17. Wright, I., Ziegler, A.: The standard coder: a machine learning approach to measuring the effort required to produce source code change. In: RAISE (2019)

    Google Scholar 

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We would like to thank the anonymous reviewers and Vaclav Matyas for their constructive and very helpful suggestions to improve this paper. The work is supported by the German Federal Ministry of Education and Research (BMBF) as part of the project Employee Privacy in Development and Operations (EMPRI-DEVOPS) under grant 16KIS0922K.

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Correspondence to Christian Burkert .

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Burkert, C., Ansohn McDougall, J., Federrath, H. (2022). Data Minimisation Potential for Timestamps in Git: An Empirical Analysis of User Configurations. In: Meng, W., Fischer-Hübner, S., Jensen, C.D. (eds) ICT Systems Security and Privacy Protection. SEC 2022. IFIP Advances in Information and Communication Technology, vol 648. Springer, Cham.

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  • Print ISBN: 978-3-031-06974-1

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