  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.
