10 papers
Subgroup Membership Inference Audits of Differentially Private Synthetic Text
Yidan Sun, Viktor Schlegel, Srinivasan Nandakumar +2
Synthetic data releases are increasingly proposed in the literature as a means of sharing realistic data replicas in lieu of sensitive private datasets. Even when the worst-case pr…
HealthLoopQA: A Context-Aware Question Answering Benchmark for Interpreting Wearable Monitoring Data in Diabetes Care
Yuchen Niu, Yanan Ma, Srinivasan Nandakumar +7
As medical wearables become integrated into daily chronic disease care, effectively interpreting longitudinal monitoring data is essential for patients and clinicians to understand…
Jacobian-Guided Anisotropic Noise Reshaping for Enhancing Representation Utility under Local Differential Privacy
Youngmok Ha, Viktor Schlegel, Yidan Sun +1
While Local Differential Privacy (LDP) serves as a foundational primitive for distributed data collection, its stringent randomization requirements often lead to severe degradation…
PathMem: Toward Cognition-Aligned Memory Transformation for Pathology MLLMs
Jinyue Li, Yuci Liang, Qiankun Li +7
Computational pathology demands both visual pattern recognition and dynamic integration of structured domain knowledge, including taxonomy, grading criteria, and clinical evidence.…
SynBench: A Benchmark for Differentially Private Text Generation
Yidan Sun, Viktor Schlegel, Srinivasan Nandakumar +9
Synthetic text generation with Differential Privacy (DP) guarantees emerges as a principled approach that can enable the sharing of sensitive datasets across institutional and regu…
Privacy-Preserving Generation of Clinical Narratives from Medical Terminologies
Yuping Wu, Viktor Schlegel, Warren Del-Pinto +11
In high-stakes domains such as healthcare, privacy concerns severely limit the use of real-world training data. Differentially private (DP) synthetic data offers a promising altern…
Evaluating Differentially Private Generation of Domain-Specific Text
Yidan Sun, Viktor Schlegel, Srinivasan Nandakumar +7
Generative AI offers transformative potential for high-stakes domains such as healthcare and finance, yet privacy and regulatory barriers hinder the use of real-world data. To addr…
Securing the Internet of Medical Things (IoMT): Real-World Attack Taxonomy and Practical Security Measures
Suman Deb, Emil Lupu, Emm Mic Drakakis +4
The Internet of Medical Things (IoMT) has the potential to radically improve healthcare by enabling real-time monitoring, remote diagnostics, and AI-driven decision making. However…
Generating Realistic Multi-Beat ECG Signals
Paul Pöhl, Viktor Schlegel, Hao Li +1
Generating synthetic ECG data has numerous applications in healthcare, from educational purposes to simulating scenarios and forecasting trends. While recent diffusion models excel…
Generating Synthetic Data with Formal Privacy Guarantees: State of the Art and the Road Ahead
Viktor Schlegel, Anil A Bharath, Zilong Zhao +1
Privacy-preserving synthetic data offers a promising solution to harness segregated data in high-stakes domains where information is compartmentalized for regulatory, privacy, or i…