Privacy-Preserving Text Sanitization for Distributed Agents Collaboration via Disentangled Representations
arXiv:2606.15335v1 Announce Type: new Abstract: When distributed agents exchange text across organizational boundaries, privacy leakage arises not only from explicit identifiers but also from distributional signatures such as formatting conventions, vocabulary choices, and syntactic patterns. We propose DiSan(Disentangled Sanitization), a privacy-preserving sanitization framework and a built-in component of Intern-Shannon for multi-agent collaboration. DiSan uses a two-stream encoder to factorize text into a source-invariant role subspace that preserves task semantics and a source-identifying
The proliferation of distributed AI agents increases the need for secure and private cross-organizational collaboration, making privacy-preserving text sanitization a critical and immediate challenge.
This development addresses a fundamental challenge in multi-agent collaboration by enabling secure information exchange without leaking sensitive organizational data, fostering greater interoperability and trust.
Previously, sharing unstructured text between distinct AI systems carried significant privacy risks; now, methods are emerging to sanitize this data while retaining semantic integrity, allowing for wider agent collaboration.
- · AI agent developers
- · Organizations using multi-agent systems
- · Cybersecurity firms
- · Malicious actors exploiting data leakage
- · Legacy data sharing protocols
Enhanced security and privacy for text-based communication between AI agents.
Accelerated adoption and integration of distributed AI agent systems across sensitive sectors due to improved trust.
Emergence of new multi-agent applications and business models predicated on secure, cross-organizational data sharing.
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Read at arXiv cs.CL