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arXiv Machine Learning

Protect Your Score: Contact Tracing With Differential Privacy Guarantees

The paper introduces a contact tracing algorithm that incorporates differential privacy to protect users’ risk scores from adversarial inference. By releasing each risk score with an epsilon of 1, the algorithm achieves a two- to ten-fold reduction in infection rates across two leading COVID‑19 agent‑based simulators. This work claims to be the first to provide differential privacy guarantees for risk score disclosure in COVID‑19 contact tracing.

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arXiv Machine Learning
Sep 11

DNA: Differentially private Neural Augmentation for contact tracing

The paper introduces DNA, a system that enhances decentralized contact tracing by combining statistical inference with a differentially private neural network. By ensuring the neural augmentation satisfies differential privacy, the method improves detection of potentially infected individuals even at a privacy budget of epsilon=1 per message. Simulation results suggest that this approach can reduce infection rates through more targeted testing.

By Rob Romijnders, Christos Louizos, Yuki M. Asano, Max Welling
safety
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arXiv Machine Learning
Oct 23

Automated screening of privacy risks in medical AI

The paper introduces a lightweight screening tool that evaluates individual patient vulnerability to membership inference attacks across thousands of medical AI models. By measuring attack scores for each patient, the authors found that certain records consistently rank high in risk regardless of training settings, and that stronger weight decay can reduce overall exposure but not the identity of the most at‑risk patients. The tool uses four small models to flag high‑risk patients before a production model is trained, aiming to integrate privacy assessment into clinical AI data preparation.

By Konstantinos Vrettos, Eleftherios Tzanis, Haridimos Kondylakis, Evangelia E. Vassalou, Florence X. Doo, Michail E. Klontzas
safety
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arXiv Machine Learning
Oct 16

Detectability without Interference: Efficient Canary Design for Privacy Auditing in One Run

The paper introduces IBIS, a framework for designing canaries that are efficient for one‑run privacy auditing. IBIS optimizes each canary’s membership inference attack score while minimizing interference among canaries by encouraging orthogonality in embedding space. The method uses influence‑based greedy initialization and a bilevel optimization algorithm that updates a single model incrementally, achieving better performance‑cost trade‑offs and reducing computational cost by up to two orders of magnitude compared to existing approaches.

By Mathieu Dagr\'eou, Valentin Dorseuil, Mohammad Yaghini, Nicolas Papernot, Aur\'elien Bellet
ragsafety
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arXiv Machine Learning
Jun 15

Let's Ask Gauss: Improved One-Run Privacy Auditing

arXiv:2606. 12733v2 Announce Type: replace Abstract: Privacy auditing provides an important safeguard by estimating the actual information leaked by a model, thus ensuring that theoretical privacy guarantees hold in practice.

By Adya Agrawal, Yu Wei, Jaspal Singh, Malik Magdon-Ismail, Vassilis Zikas
safety
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arXiv AI
Oct 1

Aegis: Generative Gradient Masking for Privacy-Preserving Medical Federated Learning

Aegis is a client‑side defense for medical federated learning that protects against model inversion attacks by adding a masking gradient derived from locally synthesized data. The method exploits the fact that attacks fail when the effective batch size exceeds the model’s leakage capacity, turning this bottleneck into a privacy guarantee. Experiments on MNIST, CIFAR‑10, and MedMNIST datasets show that Aegis neutralizes state‑of‑the‑art attacks while preserving model accuracy and adding only modest overhead.

By Chaoyu Zhang, Shanghao Shi, Heng Jin, Ning Wang, Y. Thomas Hou, Wenjing Lou
efficiencybenchmarkssafety
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arXiv Machine Learning
Jun 19

Predictability as a Fine-Grained Measure for Privacy

arXiv:2606. 20546v1 Announce Type: new Abstract: Differential privacy (DP) ensures rigorous individual-level privacy guarantees against even the most knowledgeable attackers, but its worst-case nature can impose a costly privacy-accuracy tradeoff.

By Linda Lu, Karthik Sridharan
safety
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