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Trust-Aware Adaptive Disclosure for Inference Privacy Preservation in Multi-Agent Networks — arXiv:2609.05340

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.05340
License (as archived): CC0-1.0
Archived: 2026-10-16T22:18:18.025Z
SHA-256 of the archived text: 60b331d54b15bed561b53cdaddc0518c03a45a57b5555e07c34c8cf1acd82278
Stable URL of this page: https://metraipolis.com/documento/dc_c0ff752cda14

How to cite

Trust-Aware Adaptive Disclosure for Inference Privacy Preservation in Multi-Agent Networks — arXiv:2609.05340 — arXiv (en), https://arxiv.org/abs/2609.05340 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c0ff752cda14 (Metraipolis archive dc_c0ff752cda14, sha256 60b331d54b15bed561b53cdaddc0518c03a45a57b5555e07c34c8cf1acd82278)

BibTeX

@misc{dcc0ff752cda14,
  title = {Trust-Aware Adaptive Disclosure for Inference Privacy Preservation in Multi-Agent Networks — arXiv:2609.05340},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.05340},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c0ff752cda14; sha256 60b331d54b15bed561b53cdaddc0518c03a45a57b5555e07c34c8cf1acd82278},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.05340. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Agent based systems are increasingly deployed in information critical systems including healthcare management systems, and smart grids. In this paper, we consider a multi-agent system where each agent has a latent goal that needs to be kept hidden from observing adversaries. More specifically, this paper studies privacy-preserving consensus in networked multi-agent systems under goal inference attacks. We propose a Trust-Aware Privacy Control framework that adapts message disclosure based on the dynamic trust relationships between agents. The proposed method controls information release using a trust-dependent stochastic policy. This enables a tradeoff between consensus performance and privacy preservation. Experiments demonstrate that the proposed method reduces adversarial goal inference accuracy compared to representative baselines, while maintaining competitive consensus utility, thereby highlighting the effectiveness of trust-aware mechanisms in privacy preservation of the agents in multi-agent systems.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-07 in cs.MA).
Categorie arXiv: Multiagent Systems (cs.MA) ; Cryptography and Security (cs.CR).
Pagina del lavoro: https://arxiv.org/abs/2609.05340 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T22:09:18.996Z dalla pagina https://arxiv.org/catchup/cs.MA/2026-09-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).