He Gu

• Gu, He; Plagemann, Thomas Peter; Goebel, Vera Hermine; Benndorf, Maik & Koldehofe, Boris (2023). Differential Privacy for Protecting Private Patterns in Data Streams. I O´Connor, Lisa (Red.), 2023 IEEE 39th International Conference on Data Engineering Workshops (ICDEW). IEEE (Institute of Electrical and Electronics Engineers). ISSN 979-8-3503-2244-6. s. 118–124. doi: 10.1109/ICDEW58674.2023.00025. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Gu, He (2023). Towards Guaranteed Privacy in Stream Processing: Differential Privacy for Private Pattern Protection. I Schiavoni, Valerio; Riviere, Etienne & Kemme, Bettina (Red.), DEBS'23: Proceedings of the 17th ACM International Conference on Distributed and Event-Based Systems. Association for Computing Machinery (ACM). ISSN 979-8-4007-0122-1. s. 207–210. doi: https:/doi.org/10.1145/3583678.3603284. Vis sammendrag

  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.

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