In daily life, we use numerous real-time services, such as health or fitness apps. These services 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.
The goal of this Master’s Thesis is an ontology of existing PPMs. Therefore:
- You will familiarise yourself with the topics of SPSs, PPMs and ontologies,
- You will determine the characteristics of PPMs, such as the supported datatype, the achievable privacy level, and the utility,
- Based on these preliminaries, you will analyze existing PPMs and create an ontology to represent this information.
Required/ Recommended Qualification:
The candidate:
- Should have some knowledge about ontologies,
- Has interest or experience in SPSs,
- Has interest or expertise in data privacy and PPMs.
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). https://www.mn.uio.no/ifi/english/research/projects/parrot/index.html
• 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)]
Ontology
• [Hofweber, T. (2004). Logic and Ontology. https://plato.stanford.edu/entries/ logic-ontology/ (Retrieved 7 September 2023)]
• [Jones, D., Bench-Capon, T., & Visser, P. (1998). Methodologies for Ontology Development.]
• [Uschold, M., & Gruninger, M. (1996). Ontologies: Principles, methods, and applications. The Knowledge Engineering Review, 11(2), 93–136. https://doi.org/10.1017/S0269888900007797]
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 privacy preserving 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 the 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]