  Complex event processing (CEP) is a powerful and increasingly more important
tool to analyse data streams for Internet of Things (IoT) applications. These
data streams often contain private information that requires proper protection.
However, privacy protection in CEP systems is still in its infancy, and most
existing privacy-preserving mechanisms (PPMs) are adopted from those designed
for data streams. Such approaches undermine the quality of the entire data
stream and limit the performance of IoT applications. In this paper, we attempt
to break the limitation and establish a new foundation for PPMs of CEP by
proposing a novel pattern-level differential privacy (DP) guarantee. We
introduce two PPMs that guarantee pattern-level DP. They operate only on data
that correlate with private patterns rather than on the entire data stream,
leading to higher data quality. One of the PPMs provides adaptive privacy
protection and brings more granularity and generalization. We evaluate the
performance of the proposed PPMs with two experiments on a real-world dataset
and on a synthetic dataset. The results of the experiments indicate that our
proposed privacy guarantee and its PPMs can deliver better data quality under
equally strong privacy guarantees, compared to multiple well-known PPMs
designed for data streams.
