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Author

Wenjie Qu

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Preprint Sep 2026

Towards TEE-Certified DP: Verifiable Differentially Private Training on Legacy GPUs

Wide adoption of machine learning has created growing policy and regulatory demand for protecting sensitive training data, with differential privacy (DP) emerging as a key mechanism. Yet a less-studied problem is how to certify the faithful execution of DP during training: an external verifier should be able to check t...

Li Ge, Wenjie Qu, Weitao Feng et al. · 0 citations
Jun 2026

Understanding and Evaluating Claw-like Agent Security Through a Computer-Systems Lens

SafeClawArena is developed, a benchmark of 406 adversarial tasks executed in containerized replicas of real agent platforms with canary-marked credentials and evaluated via automated taint tracking across nine output channels, exposing the inadequacy of current defenses and suggesting directions for future hardening.

Peizhi Niu, Wenjie Qu, Shangding Gu et al. · 2 citations

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