Narriva generates behavior-grounded text personas from survey data that achieve up to 87% accuracy in predicting privacy decisions, improve 6-17 points over baselines, cut tokens by 80-95%, and reproduce aggregate distributions across different studies.
Personalizing agent privacy decisions via logical entailment
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PrivacySIM shows that conditioning LLMs on user personas like demographics and attitudes improves simulation of privacy choices but reaches only 40.4% accuracy against real responses from 1,000 users.
GAAP guarantees confidentiality of private user data for AI agents by enforcing user-specified permissions deterministically through persistent information flow tracking, without trusting the agent or requiring attack-free models.
citing papers explorer
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Text-Based Personas for Simulating User Privacy Decisions
Narriva generates behavior-grounded text personas from survey data that achieve up to 87% accuracy in predicting privacy decisions, improve 6-17 points over baselines, cut tokens by 80-95%, and reproduce aggregate distributions across different studies.
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PrivacySIM: Evaluating LLM Simulation of User Privacy Behavior
PrivacySIM shows that conditioning LLMs on user personas like demographics and attitudes improves simulation of privacy choices but reaches only 40.4% accuracy against real responses from 1,000 users.
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An AI Agent Execution Environment to Safeguard User Data
GAAP guarantees confidentiality of private user data for AI agents by enforcing user-specified permissions deterministically through persistent information flow tracking, without trusting the agent or requiring attack-free models.