Share No More Than the Request Requires: Federated Disclosure for Perspective-Aware AI
Not on the map. Why?
Abstract (arXiv)
Modern AI systems bring societal risks such as mass surveillance, extreme concentrations of power, and loss of user autonomy---calling into question a model where third-parties collect and control massive amounts of user data. Users require a sovereign system to securely own, govern, and disclose their context while remaining compliant across regulated domains with strict provenance, interpretability, and policy adherence. Perspective-aware AI approaches this by transforming a user's aggregated personal data into a structured identity model called a Chronicle: a temporal knowledge graph that represents and grows with the user. Chronicles support the secure disclosure of context across federated networks. A Chronicle holder may expose a queryable, authorized view that a third-party agent may consult without centralizing anyone's data. This paper explores the problem of minimum-necessary disclosure across domain boundaries: when a requester's agent queries a Chronicle, how can the system constrain its response to release only what the requester's relationship, stated purpose, and specific task require? We propose Provenance Preserving Chronicles (PPC), a federated protocol that compiles each holder's Chronicle into a compact authorized evidence subgraph governed by one rule: share no more than the request requires. Holders keep local sovereignty; an access controller projects relationship-aware views over domain-expert ontologies; and a two-phase flow returns provenance-linked text first, releasing raw artifacts only after explicit holder approval. We frame the problem, map gaps in blockchain, P2P, and holder-sovereign designs, define the core constructs, and sketch the protocol with an explicit threat model.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 51 references that are in the map. First posted on arXiv 24 Jul 2026.
Builds on
8 of 51 references.
References · 8
- 2024AirGapAgent: Protecting Privacy-Conscious Conversational Agentscited by 90
- 2014IPFS - Content Addressed, Versioned, P2P File Systemcited by 67
- 2024Operationalizing Contextual Integrity in Privacy-Conscious Assistantscited by 36
- 2025ID-RAG: Identity Retrieval-Augmented Generation for Long-Horizon Persona Coherence in Generative Agentscited by 7
- 2022Design and Evaluation of IPFS: A Storage Layer for the Decentralized Webcited by 6
- 2022SoK: TEE-assisted Confidential Smart Contractcited by 3
- 2025Perspective-Aware AI in Extended Realitycited by 3
- 2024The Forking Way: When TEEs Meet Consensuscited by 2
Built on by
Not cited yet.