Personalizing Agent Privacy Decisions via Logical Entailment#

Authors: James Flemings (University of Southern California), Ren Yi (Google), Octavian Suciu (Google), Kassem Fawaz (University of Wisconsin--Madison), Murali Annavaram (University of Southern California), Marco Gruteser (Google)

• Why: This is the agentic-identity paper of the program. As AI agents act on users' behalf, the question shifts from authentication ("is this agent who it says it is") to authorization policy ("what may it disclose, to whom, in this context"). A Google co-authorship suggests the problem is real at production scale.
• What: Apparently a formal-logic approach to encoding user privacy preferences and checking whether a proposed agent action follows from them — turning agent decisions into a personalized policy-entailment problem (title/author line only — no abstract in program).
• How: Inference: user preferences expressed as logical rules; an agent's intended action (or data disclosure) is evaluated by entailment against those rules, with personalization meaning the rule set is learned/derived per user.
• Where applicable: Assistant/agent platforms and any system where delegated AI acts for a user — the natural companion to SPIFFE-style workload identity: proving who the agent is only matters once you can also decide what it may do.