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.
arXiv preprint arXiv:2601.08739 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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Opal enables private long-term memory for personal AI by decoupling reasoning to a trusted enclave with a lightweight knowledge graph and piggybacking reindexing on ORAM accesses.
AHGCDD distills large hypergraphs into informative synthetic versions via anchor-guided joint optimization and dual-level discrimination, achieving better effectiveness and efficiency than prior decoupled HGC approaches.
citing papers explorer
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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.
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Opal: Private Memory for Personal AI
Opal enables private long-term memory for personal AI by decoupling reasoning to a trusted enclave with a lightweight knowledge graph and piggybacking reindexing on ORAM accesses.
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Anchor-guided Hypergraph Condensation with Dual-level Discrimination
AHGCDD distills large hypergraphs into informative synthetic versions via anchor-guided joint optimization and dual-level discrimination, achieving better effectiveness and efficiency than prior decoupled HGC approaches.