Abstract
In the near future, more than two thirds of the world’s population is expected to be living in cities. In this interconnected world, data collection from various sensors is eased up and unavoidable. Handling the right data is an important factor for decision making and improving services. While at the same time keeping the right level of privacy for end users is crucial. This position paper discusses the necessary trade-off between privacy needs and data handling for the improvement of services. Pseudo-anonymization techniques have shown their limits and local computation and aggregation of data seems the way to go. To illustrate the opportunity, the case for a novel generation of clustering algorithms is made that implements a privacy by design approach. Preliminary results of such a clustering algorithm use case show that our approach exhibits a high degree of elasticity.
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Acknowledgment
This work has been partially funded by the joint research programme University of Luxembourg/SnT-ILNAS on Digital Trust for Smart-ICT.
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Brust, M.R., Bouvry, P., Danoy, G., Talbi, EG. (2020). Design Challenges of Trustworthy Artificial Intelligence Learning Systems. In: Sitek, P., Pietranik, M., Krótkiewicz, M., Srinilta, C. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Communications in Computer and Information Science, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-15-3380-8_50
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DOI: https://doi.org/10.1007/978-981-15-3380-8_50
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