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No One Size (PPM) Fits All: Towards Privacy in Stream Processing Systems

Mikhail Fomichev; Manisha Luthra; Maik Benndorf; Pratyush Agnihotri
In: Proceedings of the 17th ACM International Conference on Distributed and Event-Based Systems. ACM International Conference on Distributed and Event-Based Systems (DEBS-2023), Europe, Switzerland, DEBS '23, ISBN 9798400701221, Association for Computing Machinery, 2023.


Stream processing systems (SPSs) have been designed to process data streams in real-time, allowing organizations to analyze and act upon data on-the-fly, as it is generated. However, handling sensitive or personal data in these multilayered SPSs that distribute resources across sensor, fog, and cloud layers raises privacy concerns, as the data may be subject to unauthorized access and attacks that can violate user privacy, hence facing regulations such as the GDPR across the SPS layers. To address these issues, different privacy-preserving mechanisms (PPMs) are proposed to protect user privacy in SPSs. Yet, selecting and applying such PPMs in SPSs is challenging, since they must operate in real-time while tolerating little overhead. The multilayered nature of SPSs complicates privacy protection because each layer may confront different privacy threats, which must be addressed by specific PPMs. To overcome these challenges, we present Prinseps, our comprehensive privacy vision for SPSs. Towards this vision, we (1) identify critical privacy threats on different layers of the multilayered SPS, (2) evaluate the effectiveness of existing PPMs in addressing such threats, and (3) integrate privacy considerations into the decision-making processes of SPSs.