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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Sep 1·5 min read
GuardianAgent: Policy-Conditioned Risk-Adaptive Anonymization with Verified Adversarial Escalation
TL;DR
GuardianAgent is a novel anonymization framework that adapts privacy measures based on risk assessment and policy compliance, achieving over 0.90 privacy across multiple domains. It combines structured risk evaluation with adaptive rewriting to optimize privacy-utility trade-offs.
✦ Why It Matters
Implement GuardianAgent to enhance your application's privacy compliance and data protection strategies today.
Key Takeaways
- Utilize GuardianAgent for adaptive privacy measures in web traffic.
- Implement AMRSF for structured risk assessment in anonymization.
- Leverage fast and slow paths for efficient risk evaluation.
- Test anonymization strength based on recipient context and purpose.
How It Works
GuardianAgent combines structured risk assessment with adaptive anonymization. It calculates risk using AMRSF, which evaluates multiple factors, including the likelihood of policy violations and data sensitivity.
For efficient processing, it employs a fast path for low-risk scenarios and a slow path involving an LLM for uncertain cases. The framework's rewriting mechanism is guided by a verified adversarial guesser, which ensures that escalations in anonymization are justified by the original content.