Towards Guaranteed Privacy in Stream Processing: Differential Privacy for Private Pattern Protection
He Gu · 2023
Sensor data often contain private information that requires proper protection. Most existing privacy-preserving mechanisms (PPMs) for data streams undermine the utility of the entire data stream and limit the performance of data-driven applications. We attempt to break the limitation and establish a new foundation for PPMs by proposing novel pattern-level differential privacy (DP) guarantees and pattern-level PPMs that fulfill pattern-level DP. They operate only on data that correlate with private patterns rather than on the entire data stream, leading to higher data utility. We first describe results for sequence operator based patterns in a centralized system and outline future work to generalize it for other operators and to local solutions.