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Parrot: Privacy Engineering for Real-Time Analytics in Human-Centered Internet of Things

Logo with the letters "Parrot" where the P is adapted to indicate the head of the parrot bird


Big data applications promise to offer smart solutions to many urgent societal challenges such as health care, traffic coordination, energy management, etc. The basic premise for these applications is “the more data the better”. Theoretically, any smart-phone and -watch owner could be a continuous source of valuable data and contribute to many useful big data applications. However, such data can reveal a lot of sensible information, like the current location or the heart rate of the owner of such devices. Protection of personal data is important in our society and for example manifested in the EU General Data Protection Regulation (GDPR). However, privacy protection and useful big data applications are hard to bring together. Implementing proper privacy protection requires skills that are typically not in the focus of data analysts and big data developers. Thus, many individuals tend to share none of their data if in doubt whether it will be properly protected. There exist excellent privacy solutions between the “all or nothing” approach. For example, instead of continuously publishing the current location of individuals one might aggregate this data and only publish information of how many individuals are in a certain area of the city. Thus, personal data is not revealed, while useful information for certain applications like traffic coordination is retained.

The goal of the Parrot project is providing tools for real-time data analysis applications that leverage this “middle ground”. Data analysts should only be required to specify their data needs and end-users can select the privacy requirements for their data as well as the applications and end-users they want to share their data with. The project results are expected to enable the (semi-)automatic integration of appropriate privacy protection into real-time data stream applications. Thus, individuals can safely provide data which in turn improves the results of big data applications.

The Parrot project is an international collaboration between the University of Oslo (Norway), the Technical University of Darmstadt (Germany), and the University of Groningen (The Netherlands) funded by the Research Council of Norway (2020 – 2023) with three PhD and one PostDoc position. To properly support this collaboration regular exchanges between the teams in Oslo and Darmstadt are organized and funded by the project. Scientific and technical challenges that are addressed in this research project include

  • Expressing and matching of privacy concerns of end-users and data quality requirements for data analytic.
  • Characterizing privacy protecting mechanisms in terms of protection level and impact onto data quality.
  • (Semi-)automatic rewriting of queries for Complex Event Processing (CEP) systems and automatic placement of operator graphs in distributed (mobile) CEP systems under security and privacy concerns.
  • Design and implementation of a fully decentralized and trustworthy overlay for distributed CEP systems.
  • Experimental and empirical evaluation of solutions developed in the project.

For further inquiries concerning the Parrot project please contact the project manager Professor Thomas Plagemann at the University of Oslo (, Professor Matthias Hollick at the Technical University of Darmstadt (, or Professor Boris Koldehofe at the University of Groningen (

Selected publications

Lindeberg, Morten Gunnar Bjørner & Plagemann, Thomas Peter (2022). A Study on Migration Scheduling in Distributed Stream Processing Engines, In Proceedings of the 23rd International Conference on Distributed Computing and Networking (ICDCN 22). ACM. Association for Computing Machinery (ACM). ISSN 978-1-4503-9560-1.

Volnes, Espen; Plagemann, Thomas Peter; Boris, Koldehofe & Goebel, Vera Hermine (2022). Travel light: state shedding for efficient operator migration. In Yonghuan, Zhou (Eds.), DEBS '22: Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems. Association for Computing Machinery (ACM). ISSN 978-1-4503-9308-9. p. 79–84.

Mikhail Fomichev, Luis F. Abanto-leon, Max Stiegler, Alejandro Molina, Jakob Link, and Matthias Hollick. 2022. Next2You: Robust Copresence Detection Based on Channel State Information. ACM Trans. Internet Things 3, 2, Article 11 (May 2022), 31 pages.

Mikhail Fomichev, Julia Hesse, Lars Almon, Timm Lippert, Jun Han, and Matthias Hollick. 2021. FastZIP: faster and more secure zero-interaction pairing. In Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys '21). Association for Computing Machinery, New York, NY, USA, 440–452.

Boughzala, Bochra & Koldehofe, Boris (2021). Accelerating the performance of data analytics using network-centric processing. In Valle, Emanuele Della & Alessandro, Margara (Ed.), DEBS '21: Proceedings of the 15th ACM International Conference on Distributed and Event-based Systems. ACM Digital Library. ISSN 9781450385558. doi: 10.1145/3465480.3468162.

Fomichev, Mikhail; Hesse, Julia; Almon, Lars; Lippert, Timm; Ha, Jun & Hollick, Matthias (2021). FastZIP: faster and more secure zero-interaction pairing. In Banerjee, Suman (Eds.), MobiSys '21: The 19th Annual International Conference on Mobile Systems, Applications, and Services. Association for Computing Machinery (ACM). ISSN 9781450384438. doi: 10.1145/3458864.3467883.


Tags: data stream processing, privacy, utility
Published June 24, 2020 9:16 PM - Last modified Oct. 21, 2022 11:57 AM