C-mDP augments the secret with context, enforces metric indistinguishability on the augmented domain, and reduces the LP via conditional independence to achieve higher utility than standard mDP on vehicle traces.
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A data-centric survey finds that only information-flow control covers compositional and cross-session leakage in LLM agents and that no single benchmark tests an agent across all its data surfaces under one policy.
citing papers explorer
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Context-Aware Metric Differential Privacy for Vehicle Trajectory Data
C-mDP augments the secret with context, enforces metric indistinguishability on the augmented domain, and reduces the LP via conditional independence to achieve higher utility than standard mDP on vehicle traces.
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Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents
A data-centric survey finds that only information-flow control covers compositional and cross-session leakage in LLM agents and that no single benchmark tests an agent across all its data surfaces under one policy.
- SoK: Practical Aspects of Releasing Differentially Private Graphs