PrivScope: Task-scoped Disclosure Control for Hybrid Agentic Systems
📰 ArXiv cs.AI
Learn how PrivScope controls disclosure in hybrid agentic systems to prevent over-disclosure of sensitive information
Action Steps
- Implement task-scoped disclosure control using PrivScope to minimize over-disclosure
- Configure hybrid agentic systems to enrich user requests with context from persistent working state
- Use cloud language models (CLMs) to delegate capability-intensive subtasks while controlling disclosure
- Test PrivScope with various task scenarios to evaluate its effectiveness in preventing over-disclosure
- Apply PrivScope to real-world applications to protect sensitive information and improve overall system security
Who Needs to Know This
AI engineers and researchers working on hybrid agentic systems can benefit from this knowledge to improve the security and privacy of their systems
Key Insight
💡 PrivScope helps prevent over-disclosure of sensitive information in hybrid agentic systems by controlling the disclosure of task-irrelevant context and sensitive details
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🚀 Introducing PrivScope: Task-scoped disclosure control for hybrid agentic systems to prevent over-disclosure of sensitive info 🤫
Key Takeaways
Learn how PrivScope controls disclosure in hybrid agentic systems to prevent over-disclosure of sensitive information
Full Article
Title: PrivScope: Task-scoped Disclosure Control for Hybrid Agentic Systems
Abstract:
arXiv:2605.16630v2 Announce Type: cross Abstract: Hybrid local--cloud agents enrich user requests with context from persistent working state before delegating capability-intensive subtasks to a cloud language model (CLM). While this enrichment can improve task success, it also exposes unnecessary information in the cloud-bound payload, including task-irrelevant context, carryover from prior workflows, and overly specific sensitive details, resulting in \emph{over-disclosure}. Existing solutions
Abstract:
arXiv:2605.16630v2 Announce Type: cross Abstract: Hybrid local--cloud agents enrich user requests with context from persistent working state before delegating capability-intensive subtasks to a cloud language model (CLM). While this enrichment can improve task success, it also exposes unnecessary information in the cloud-bound payload, including task-irrelevant context, carryover from prior workflows, and overly specific sensitive details, resulting in \emph{over-disclosure}. Existing solutions
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