Trust-Aware Adaptive Disclosure for Inference Privacy Preservation in Multi-Agent Networks

The provided search context does not contain information regarding a specific framework titled "Trust-Aware Adaptive Disclosure for Inference Privacy Preservation in Multi-Agent Networks."

However, the search results do highlight several related approaches for privacy preservation in multi-agent systems:

• Information-Theoretic Privacy Control: A framework for sequential multi-agent LLM systems that uses information-theoretic methods to control privacy.
• Secure and Privacy-Preserving Consensus: A decentralized approach using partial homomorphic cryptography to embed secrecy in pairwise interactions, protecting node states without disclosing them to neighbors.
• Privacy-preserving Resilient Consensus: Methods that combine noise-based or cryptography-based techniques (like the Paillier cryptosystem) to achieve consensus while protecting initial states against faulty agents and eavesdroppers.
• Trustworthy Distributed Mirror Learning (TDML): A framework for secure multi-agent reinforcement learning that uses split advantage computation and zero-knowledge proofs to protect local data privacy and ensure verifiable message exchange.

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Trust-Aware Adaptive Disclosure for Inference Privacy Preservation in Multi-Agent Networks addresses a privacy risk that is often underemphasized in distributed coordination: even when agents exchange only seemingly benign consensus-related messages, an adversary may infer their latent goals, preferences, or internal states from the disclosure pattern. In multi-agent consensus, agents iteratively share local information to reach agreement, but that same information flow can act as a side channel. The work frames the problem as a tradeoff between coordination utility and inference privacy: too much disclosure helps convergence, while too little may degrade task performance or prevent agreement.

The central contribution is a Trust-Aware Privacy Control framework in which disclosure becomes adaptive rather than static. Agents assess the trustworthiness of neighboring agents or communication channels and condition the amount, granularity, or specificity of information they reveal on that trust estimate. High-trust peers may receive richer messages necessary for efficient consensus, whereas low-trust or potentially adversarial peers receive masked, aggregated, or otherwise constrained information that limits their ability to reconstruct private objectives. In effect, the framework couples consensus dynamics with an inference-risk model, aiming to preserve latent agent goals while maintaining sufficient information flow for network-level coordination.

This matters because many real-world multi-agent systems—robot swarms, IoT networks, edge control systems, and distributed optimization platforms—must coordinate in partially adversarial or heterogeneous environments where not all peers can be assumed benign. The paper shifts the privacy focus from payload encryption alone to the semantic content of coordination messages, which is often the true leakage vector. By tying disclosure to trust, the approach offers a principled path toward privacy-preserving consensus in open networks, where agents must balance transparency, performance, and protection of sensitive intent.