These apps provide valuable information based on data collected, e.g. using sensors of smartwatches or smartphones. In order to process the sensor data and extract the information in real-time, it is sent to and processed by stream processing systems (SPS). However, this data may contain information that goes beyond the purpose of the application and that one may wish to keep private, such as age and weight information. In SPSs, Privacy-Preserving Mechanisms (PPM) are explicitly used to protect this information, e.g. Differential Privacy adds noise to the sensor data to protect this information. By applying a PPM, higher data privacy is achieved, but at the same time, the utility of the service is affected. The challenge is to get the best balance between privacy and utility, also known as Privacy - Utility TradeOff (PUT).
This thesis aims to analyze the PUT for preselected applications and PPMs. Therefore you will:
- Familiarise yourself with the topic and select N applications for the analysis in consultation with your supervisors,
- Determine the requirements of the selected applications, e.g. based on technical specifications and documentation,
- Create a virtual testbed with which the privacy and utility of an application can be evaluated under different conditions,
- Use the testbed to assess the selected apps and analyze the outcome.
Required/ Recommended Qualification
The candidate
• Should have experience with the Android platform,
• has interest or experience with Sensors,
• has interest or experience in SPSs,
• has interest or experience in data privacy and PPMs,
• has interests in data science.
Contact persons
• Maik Benndorf (maikb@ifi.uio.no)
• Thomas Plagemann (plagemann@ifi.uio.no)
Resources
Parrot: This research project is associated with the Parrot project (Privacy Engineering for Real-Time Analytics in Human-Centered Internet of Things).
• Project Site: [UiO - Institutt for informatikk. (2020, June 24). Parrot: Privacy Engineering for Real-Time Analytics in Human-Centered Internet of Things. https://www.mn.uio.no/ifi/english/research/projects/parrot/index.html (Retrieved 7 September 2023)]
• Vision of Parrot: [Plagemann, T., Goebel, V., Hollick, M., & Koldehofe, B. (2022). Towards Privacy Engineering for Real-Time Analytics in the Human-Centered Internet of Things (arXiv:2210.16352). arXiv. http://arxiv.org/abs/2210.16352 (Retrieved 7 September 2023)]
SPS
• [Stephens, R. (1997). A survey of stream processing. Acta Informatica, 34(7), 491–541. https://doi.org/10.1007/s002360050095]
• [Isah, H., Abughofa, T., Mahfuz, S., Ajerla, D., Zulkernine, F., & Khan, S. (2019). A Survey of Distributed Data Stream Processing Frameworks. IEEE Access, 7, 154300–154316. https://doi.org/10.1109/ACCESS. 2019.2946884]
• [Fragkoulis, M., Carbone, P., Kalavri, V., & Katsifodimos, A. (2023). A Survey on the Evolution of Stream Processing Systems (arXiv:2008.00842). arXiv. http://arxiv.org/abs/2008.00842]
PPM
• [Cunha, M., Mendes, R., & Vilela, J. P. (2021). A survey of privacypreserving mechanisms for heterogeneous data types. Computer Science Review, 41, 100403. https://doi.org/10.1016/j.cosrev.2021.100403]
• [Gardiyawasam Pussewalage, H. S., & Oleshchuk, V. A. (2016). Privacy preserving mechanisms for enforcing security and privacy requirements in E-health solutions. International Journal of Information Management, 36(6, Part B), 1161–1173. https://doi.org/10.1016/j.ijinfomgt.2016.07.006]
• [Vergara-Laurens, I. J., Jaimes, L. G., & Labrador, M. A. (2017). Privacy-Preserving Mechanisms for Crowdsensing: Survey and Research Challenges. IEEE Internet of Things Journal, 4(4), 855–869. https://doi.org/10.1109/JIOT.2016.2594205]
Sensor
• Android Sensor Overview: [Google LLC. (n.d.). Sensors Overview. Android Developers. https://developer.android.com/guide/topics/sensors/ sensors_overview (Retrieved 7 September 2023)]
• Sensor Inference:
– [Kröger, J. (2019). Unexpected inferences from sensor data: a hidden privacy threat in the internet of things. In Internet of Things. Information Processing in an Increasingly Connected World: First IFIP International Cross-Domain Conference, IFIPIoT 2018, Held at the 24th IFIP World Computer Congress, WCC 2018, Poznan, Poland, September 18-19, 2018, Revised Selected Papers 1 (pp. 147-159). Springer International Publishing.]
– [Hajihassnai, O., Ardakanian, O., & Khazaei, H. (2021). Obscurenet: Learning attribute-invariant latent representation for anonymizing sensor data. Proceedings of the International Conference on Internet of Things Design and Implementation, 40–52.]
– [Moqurrab, S. A., Naeem, T., Shoaib Malik, M., Fayyaz, A. A., Jamal, A., & Srivastava, G. (2023). UtilityAware: A framework for data privacy protection in e-health. Information Sciences, 643, 119247. https://doi.org/10.1016/j.ins.2023.119247]