Lucas Lange

selected publications

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

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

   arXiv preprint (Sep. 2024)

   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.

   @misc{langeSliceItUnmasking2024,
     title = {Slice It up: {{Unmasking User Identities}} in {{Smartwatch Health Data}}},
     shorttitle = {Privacy at {{Risk}}},
     author = {Lange, Lucas and Schreieder, Tobias and Christen, Victor and Rahm, Erhard},
     year = {2024},
     month = sep,
     number = {arXiv:2308.08310},
     eprint = {2308.08310},
     primaryclass = {cs},
     publisher = {arXiv},
     doi = {10.48550/arXiv.2308.08310},
     url = {http://arxiv.org/abs/2308.08310},
     urldate = {2023-10-17},
     archiveprefix = {arXiv},
     code = {https://github.com/tobiasschreieder/dtw-attacks},
     pdf = {https://arxiv.org/pdf/2308.08310.pdf},
     keywords = {Computer Science - Cryptography and Security},
     bibtexshow = {true},
     selected = {true}
   }

2. Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning

   Lucas Lange, Maurice-Maximilian Heykeroth, and Erhard Rahm

   arXiv preprint (Sep. 2024)

   Abs Bib Pub PDF Code

   Machine Learning (ML) is crucial in many sectors, including computer vision. However, ML models trained on sensitive data face security challenges, as they can be attacked and leak information. Privacy-Preserving Machine Learning (PPML) addresses this by using Differential Privacy (DP) to balance utility and privacy. This study identifies image dataset characteristics that affect the utility and vulnerability of private and non-private Convolutional Neural Network (CNN) models. Through analyzing multiple datasets and privacy budgets, we find that imbalanced datasets increase vulnerability in minority classes, but DP mitigates this issue. Datasets with fewer classes improve both model utility and privacy, while high entropy or low Fisher Discriminant Ratio (FDR) datasets deteriorate the utility-privacy trade-off. These insights offer valuable guidance for practitioners and researchers in estimating and optimizing the utility-privacy trade-off in image datasets, helping to inform data and privacy modifications for better outcomes based on dataset characteristics.

   @misc{langeAssessingImpactImage2024,
     title = {Assessing the {{Impact}} of {{Image Dataset Features}} on {{Privacy-Preserving Machine Learning}}},
     author = {Lange, Lucas and Heykeroth, Maurice-Maximilian and Rahm, Erhard},
     year = {2024},
     month = sep,
     number = {arXiv:2409.01329},
     eprint = {2409.01329},
     primaryclass = {cs},
     publisher = {arXiv},
     doi = {10.48550/arXiv.2409.01329},
     url = {http://arxiv.org/abs/2409.01329},
     urldate = {2024-09-08},
     archiveprefix = {arXiv},
     code = {https://github.com/luckyos-code/dataset-analysis-ppml},
     pdf = {http://arxiv.org/pdf/2409.01329.pdf},
     keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Cryptography and Security,Computer Science - Machine Learning},
     bibtexshow = {true},
     selected = {true}
   }

3. Generating Synthetic Health Sensor Data for Privacy-Preserving Wearable Stress Detection

   Lucas Lange, Nils Wenzlitschke, and Erhard Rahm

   Sensors (May. 2024)

   Abs Bib Pub PDF Code

   Smartwatch health sensor data are increasingly utilized in smart health applications and patient monitoring, including stress detection. However, such medical data often comprise sensitive personal information and are resource-intensive to acquire for research purposes. In response to this challenge, we introduce the privacy-aware synthetization of multi-sensor smartwatch health readings related to moments of stress, employing Generative Adversarial Networks (GANs) and Differential Privacy (DP) safeguards. Our method not only protects patient information but also enhances data availability for research. To ensure its usefulness, we test synthetic data from multiple GANs and employ different data enhancement strategies on an actual stress detection task. Our GAN-based augmentation methods demonstrate significant improvements in model performance, with private DP training scenarios observing an 11.90–15.48% increase in F1-score, while non-private training scenarios still see a 0.45% boost. These results underline the potential of differentially private synthetic data in optimizing utility–privacy trade-offs, especially with the limited availability of real training samples. Through rigorous quality assessments, we confirm the integrity and plausibility of our synthetic data, which, however, are significantly impacted when increasing privacy requirements.

   @article{langeGeneratingSyntheticHealth2024a,
     title = {Generating {{Synthetic Health Sensor Data}} for {{Privacy-Preserving Wearable Stress Detection}}},
     author = {Lange, Lucas and Wenzlitschke, Nils and Rahm, Erhard},
     year = {2024},
     month = may,
     journal = {Sensors},
     volume = {24},
     number = {10},
     pages = {3052},
     publisher = {{Multidisciplinary Digital Publishing Institute}},
     issn = {1424-8220},
     doi = {10.3390/s24103052},
     url = {https://www.mdpi.com/1424-8220/24/10/3052},
     urldate = {2024-05-13},
     code = {https://github.com/luckyos-code/Privacy-Preserving-Smartwatch-Health-Data-Generation-Using-DP-GANs},
     copyright = {http://creativecommons.org/licenses/by/3.0/},
     langid = {english},
     pdf = {https://www.mdpi.com/1424-8220/24/10/3052/pdf},
     keywords = {differential privacy,generative adversarial network,physiological sensor data,privacy-preserving machine learning,smart health,smartwatch,stress recognition,synthetic data,time series},
     bibtexshow = {true},
     selected = {true}
   }