No One Size (PPM) Fits All: Towards Privacy in Stream Processing Systems
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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. However, selecting and applying such PPMs in SPSs is challenging, as they must operate in real-time while tolerating little overhead. The nature of multilayered SPSs complicates privacy protection, as each layer may face different privacy threats which must be addressed by specific PPMs. To overcome these challenges, we present Prinseps, a comprehensive privacy vision for SPSs. Towards this vision, we (1) identify critical privacy threats at different layers of a multilayered SPS, (2) evaluate the effectiveness of existing PPMs in tackling these threats, and (3) integrate privacy considerations into the decision-making processes of SPSs.
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