Lucas Lange

selected publications

1. Learning from Anonymized and Incomplete Tabular Data

   Lucas Lange, Adrian Böttinger, Victor Christen, Anushka Vidanage, Peter Christen, and Erhard Rahm

   arXiv preprint (Feb. 2026)

   Abs Bib Pub PDF Code

   User-driven privacy allows individuals to control whether and at what granularity their data is shared, leading to datasets that mix original, generalized, and missing values within the same records and attributes. While such representations are intuitive for privacy, they pose challenges for machine learning, which typically treats non-original values as new categories or as missing, thereby discarding generalization semantics. For learning from such tabular data, we propose novel data transformation strategies that account for heterogeneous anonymization and evaluate them alongside standard imputation and LLM-based approaches. We employ multiple datasets, privacy configurations, and deployment scenarios, demonstrating that our method reliably regains utility. Our results show that generalized values are preferable to pure suppression, that the best data preparation strategy depends on the scenario, and that consistent data representations are crucial for maintaining downstream utility. Overall, our findings highlight that effective learning is tied to the appropriate handling of anonymized values.

   @misc{langeLearningAnonymizedIncomplete2026,
     title = {Learning from {Anonymized} and {Incomplete Tabular Data}},
     author = {Lange, Lucas and B{\"o}ttinger, Adrian and Christen, Victor and Vidanage, Anushka and Christen, Peter and Rahm, Erhard},
     year = {2026},
     month = feb,
     number = {arXiv:2602.01217},
     eprint = {2602.01217},
     primaryclass = {cs},
     publisher = {arXiv},
     doi = {10.48550/arXiv.2602.01217},
     url = {http://arxiv.org/abs/2602.01217},
     urldate = {2026-02-03},
     archiveprefix = {arXiv},
     keywords = {Computer Science - Artificial Intelligence,Computer Science - Cryptography and Security,Computer Science - Databases,Computer Science - Machine Learning},
     code = {https://github.com/luckyos-code/user-driven-privacy},
     pdf = {https://arxiv.org/pdf/2602.01217},
     bibtexshow = {true},
     selected = {true}
   }

2. Slice It up: Unmasking User Identities in Smartwatch Health Data

   Lucas Lange, Tobias Schreieder, Victor Christen, and Erhard Rahm

   20th ACM ASIA Conference on Computer and Communications Security (AsiaCCS 2025) (Aug. 2025)

   Abs Bib Pub PDF Code

   Wearables are widely used for health data collection due to their availability and advanced sensors, enabling smart health applications like stress detection. However, the sensitivity of personal health data raises significant privacy concerns. While user de-identification by removing direct identifiers such as names and addresses is commonly employed to protect privacy, the data itself can still be exploited to re-identify individuals. We introduce a novel framework for similarity-based Dynamic Time Warping (DTW) re-identification attacks on time series health data. Using the WESAD dataset and two larger synthetic datasets, we demonstrate that even short segments of sensor data can achieve perfect re-identification with our Slicing-DTW-Attack. Our attack is independent of training data and computes similarity rankings in about 2 minutes for 10,000 subjects on a single CPU core. These findings highlight that de-identification alone is insufficient to protect privacy. As a defense, we show that adding random noise to the signals significantly reduces re-identification risk while only moderately affecting usability in stress detection tasks, offering a promising approach to balancing privacy and utility.

   @inproceedings{langeSliceItUnmasking2025,
     title = {Slice It up: {Unmasking User Identities} in {Smartwatch Health Data}},
     shorttitle = {Slice It Up},
     booktitle = {20th ACM ASIA Conference on Computer and Communications Security (AsiaCCS 2025)},
     author = {Lange, Lucas and Schreieder, Tobias and Christen, Victor and Rahm, Erhard},
     year = {2025},
     month = aug,
     series = {{ASIA CCS} '25},
     pages = {710--726},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     doi = {10.1145/3708821.3710834},
     url = {https://dl.acm.org/doi/10.1145/3708821.3710834},
     urldate = {2026-02-02},
     isbn = {979-8-4007-1410-8},
     code = {https://github.com/tobiasschreieder/dtw-attacks},
     pdf = {https://dl.acm.org/doi/pdf/10.1145/3708821.3710834},
     bibtexshow = {true},
     selected = {true}
   }