He Gu

• Gu, He; Plagemann, Thomas Peter & Goebel, Vera Hermine (2026). A Seed for Privacy - semi-automatic privacy-revealing data detection in databases and data streams. In Lofstead, Jay & Kirkpatrick, Christine (Ed.), Proceedings of the 38th International Conference on Scalable Scientific Data Management. Association for Computing Machinery (ACM). ISSN 9798400727085. p. 1–12. doi: 10.1145/3828820.3828821. Full text in Research Archive Show summary

  Sharing databases and data streams imposes the danger of revealing private information in the form of complex events which can comprise individual data elements and their combinations. Identifying these privacy-revealing complex events is crucial for preserving privacy while maintaining data utility. However, data producers often lack the expertise to comprehensively identify these events, which undermines many state-of-the-art privacy-preserving mechanisms that rely on accurate event labeling. To address this challenge, we developed pArborist - a tool that can semi-automatically create a set of queries to identify and label privacy-revealing complex events in both static datasets and dynamic data streams, guided by the privacy requirements of the data producer. pArborist uses the schema of the database or data stream combined with initial input from the data producer, i.e., seed queries. From each seed query, pArborist grows a tree containing all possible syntactically correct queries, constrained by an upper limit on computational resources. Following this growing phase, the tree is refined by eliminating queries that lack correlation to the seed or are conditionally independent of the seed. Our evaluation indicates that pArborist achieves overall recall of 90% and precision of 93% in finding privacy-revealing queries, and this significantly surpasses the state-of-the-art approach FQID. In data stream processing experiments, pArborist introduces a delay of approximately 1.3 ms following an average warm-up period of 920 ms. The experiments also show that pArborist can automatically detect privacy-revealing complex events according to GDPR.

• Gu, He; Plagemann, Thomas Peter; Goebel, Vera Hermine; Benndorf, Maik & Koldehofe, Boris (2023). Differential Privacy for Protecting Private Patterns in Data Streams. In O´Connor, Lisa (Eds.), 2023 IEEE 39th International Conference on Data Engineering Workshops (ICDEW). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350322446. p. 118–124. doi: 10.1109/ICDEW58674.2023.00025. Full text in Research Archive Show summary

  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. In Schiavoni, Valerio; Riviere, Etienne & Kemme, Bettina (Ed.), DEBS'23: Proceedings of the 17th ACM International Conference on Distributed and Event-Based Systems. Association for Computing Machinery (ACM). ISSN 9798400701221. p. 207–210. doi: 10.1145/3583678.3603284. Full text in Research Archive Show summary

  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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• Plagemann, Thomas Peter & Gu, He (2025). Data Sharing and Privacy - An Empirical Study. Full text in Research Archive
• Gu, He (2024). Pattern-level Differential Privacy Solutions for Transparent and Opaque Data Stream Processing. Full text in Research Archive
• Gu, He; Plagemann, Thomas Peter; Goebel, Vera Hermine; Benndorf, Maik & Koldehofe, Boris (2026). Pattern-level Differential Privacy for High-utility Complex Event Processing. arxiv.org. doi: 10.48550/ARXIV.2609.23827. Full text in Research Archive Show summary

  Current privacy-preserving mechanisms (PPMs) in Complex Event Processing (CEP) systems are unnecessarily restrictive, reducing the utility of data received by data consumers. This article presents a novel approach to preserve privacy in CEP systems, improving the utility of detected event patterns by dynamically adapting the noise added to an unprotected data stream. We introduce a new guarantee named pattern-level differential privacy (DP), which enables us to apply and compare the strength of PPMs at the pattern level. We propose new pattern-level PPMs yielding pattern-level DP and analyze different trust settings of these PPMs and their requirements for context knowledge in the CEP system, e.g., the deployed queries. Our evaluation is based on three datasets (two real-world, one synthetic) and shows that the proposed PPMs increase data utility while preserving the same privacy level as the state-of-the-art PPMs. We use simulations to study the performance of our proposed PPMs in various practical scenarios. Furthermore, we demonstrate that computational complexity is not an obstacle to deployment.

• Gu, He (2026). Privacy-preserving Complex Event Processing with Privacy Guarantees and Improved Data Utility. Universitetet i Oslo. Full text in Research Archive Show summary

  Internet of Things and data-driven applications have become increasingly ubiquitous, raising great concerns in potential privacy disclosures in data streams. This makes privacy protection one of the most essential research topics. However, protecting privacy means to conceal private information, which may also distort other useful information and reduce data utility. Therefore, this thesis presents novel approaches that protect privacy in data streams with less data distortion and higher utility than existing research. The privacy protection is based on Differential Privacy – a de facto standard of privacy protection in data management. Our approaches have the potential to surpass other existing solutions because we only focus on protecting private patterns that may reveal private information instead of protecting all data. We also present a semi-automatic approach which detects private patterns in databases or data streams. This approach can be implemented with only marginal privacy expertise, or even with no privacy expertise if a generally accepted regulation/law is appointed to be followed, e.g., The General Data Protection Regulation (GDPR). This approach simplifies the procedure of privacy-aware data-sharing in both human life and research. All our approaches are evaluated with public and synthetic datasets and are proven better than existing solutions for these datasets.

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