User Interaction Data in Apps: Comparing Policy Claims to Implementations

Enck, W., Gilbert, P., Han, S., Tendulkar, V., Chun, B.G., Cox, L.P., Jung, J., McDaniel, P., Sheth, A.N.: Taintdroid: an information-flow tracking system for realtime privacy monitoring on smartphones. ACM Transactions on Computer Systems (TOCS) 32

2014

Earlier work this paper cites.

Qu, Z., Rastogi, V., Zhang, X., Chen, Y., Zhu, T., Chen, Z.: Autocog: Measuring the description-to-permission fidelity in android applications. In: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security. pp. 1354–1365 (2014)

2014

Earlier work this paper cites.

Avdiienko, V., Kuznetsov, K., Gorla, A., Zeller, A., Arzt, S., Rasthofer, S., Bodden, E.: Mining apps for abnormal usage of sensitive data. In: The 37th IEEE international conference on software engineering. vol. 1, pp. 426–436. IEEE (2015)

2015

Earlier work this paper cites.

Tesfay, W.B., Hofmann, P., Nakamura, T., Kiyomoto, S., Serna, J.: PrivacyGuide: Towards an Implementation of the EU GDPR on Internet Privacy Policy Evaluation. In: Proceedings of the Fourth ACM International Workshop on Security and Privacy Analytics. p. 15–21. IWSPA ’18 (2018)

2018

Earlier work this paper cites.

Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding (2019)

2019

Earlier work this paper cites.

Zimmeck, S., Story, P., Smullen, D., Ravichander, A., Wang, Z., Reidenberg, J.R., Russell, N.C., Sadeh, N.: Maps: Scaling privacy compliance analysis to a million apps. Proc. Priv. Enhancing Tech. 2019

2019

Earlier work this paper cites.

Zhang, X., Wang, X., Slavin, R., Breaux, T., Niu, J.: How does misconfiguration of analytic services compromise mobile privacy? In: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering. pp. 1572–1583 (2020)

2020

Cited alongside, same era.

Grünewald, E., Pallas, F.: TILT: A GDPR-aligned transparency information language and toolkit for practical privacy engineering. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. pp. 636–646 (2021)

2021

Cited alongside, same era.

Leiva, L.A., Arapakis, I., Iordanou, C.: My mouse, my rules: Privacy issues of behavioral user profiling via mouse tracking. In: Proceedings of the 2021 Conference on Human Information Interaction and Retrieval. pp. 51–61 (2021)

2021

Cited alongside, same era.

Ravichander, A., Black, A.W., Norton, T., Wilson, S., Sadeh, N.: Breaking Down Walls of Text: How Can NLP Benefit Consumer Privacy? In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. vol. 1 (2021)

Creţu, A.M., Monti, F., Marrone, S., Dong, X., Bronstein, M., de Montjoye, Y.A.: Interaction data are identifiable even across long periods of time. Nature Communications 13

2022

Later among the works it cites.

Marda, V.: Non-personal data: the case of the Indian Data Protection Bill, definitions and assumptions (2020), https://www.adalovelaceinstitute.org/blog/non-personal-data-indian-data-protection-bill/ , (Accessed on 28/11/2023)

2023

Closest in time.

Rzhevkina, A.: Several EU countries banned Google Analytics - here are some alternatives. https://www.contentgrip.com/eu-countries-ban-google-analytics/ (September 2022), (Accessed on 03/11/2023)

2023

Closest in time.

Singh, A., Raghavan, M., Chugh, B., Prasad, S.: The Contours of Public Policy for Non-Personal Data Flows in India (2019), https://www.dvara.com/research/blog/2019/09/24/the-contours-of-public-policy-for-non-personal-data-flows-in-india/ , (Accessed on 28/11/2023)

2023

Closest in time.