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Silence Laboratories

#+BEGIN_SRC text BLOCK A — Silence Labs “A2A-CCVM” whitepaper, fast recap and due-diligence

TL;DR The doc proposes adding a cryptographic computing layer to Google’s Agent-to-Agent (A2A) protocol so agents from different orgs can compute on each other’s private data without revealing raw inputs or proprietary models, using MPC, HE, and ZK. Target outcomes: private enrichment, joint risk scoring, compliant cross-company workflows. Silence Laboratories. oai_citation:0‡md.silencelaboratories.com

What it is An “Agent-to-Agent Cryptographic Computing VM” (A2A-CCVM) that plugs into A2A message paths. Each org’s agent routes sensitive payloads through a local CCVM that engages a peer CCVM to run two-party secure computation, returning only minimal outputs such as flags or prices. The doc frames this as making confidentiality a feature rather than a trade-off. Silence Laboratories. oai_citation:1‡md.silencelaboratories.com

Why now A2A adoption is projected to scale, with many vendors committing to agent interoperability. The paper argues that privacy, IP protection, and compliance are the main brake, since naive A2A spreads plaintext across logs, observability, and downstream agents. Hence “programmable privacy” becomes required infrastructure. Silence Laboratories. oai_citation:2‡md.silencelaboratories.com

Core capabilities the paper claims • Programmable privacy via MPC, homomorphic encryption, and zero-knowledge proofs.
• Industry-agnostic patterns such as private set intersection, secure supplier evaluation, and joint fraud detection.
• “Regulatory readiness” through verifiable consent and mathematical assurances.
• A performance lineage from Silence’s production MPC stack with sub-20 ms threshold signatures in digital-asset deployments.
Silence Laboratories. oai_citation:3‡md.silencelaboratories.com

Architecture sketch Agents keep their own private data, invoke a local CCVM, then run a two-party secure compute with a peer CCVM across the A2A channel. The diagram shows local LLM stacks (e.g., Vertex AI/Gemini) and internal APIs accessed via MCP, with the CCVM insulating cross-org exchanges. Deliverable is only the agreed aggregate or decision signal. Silence Laboratories. oai_citation:4‡md.silencelaboratories.com

What problems it says legacy controls fail at Static roles, one-time consent, silo walls, long-lived OAuth scopes, regex DLP, and after-the-fact audits do not match agent speed or combinatorial topologies, so leakage occurs before humans can review. The pitch: enforce privacy in real time with cryptography and code, not contracts. Silence Laboratories. oai_citation:5‡md.silencelaboratories.com

Use-case vignettes called out • Cross-bank fraud teams: apply Bank A’s proprietary model to Bank B’s live transactions without revealing weights or raw events.
• Healthcare: combine an FDA-cleared imaging model with external genomic inputs while keeping PHI siloed.
• Retail–supplier: compute demand elasticity and clearing prices without exposing cost curves or customer data.
Silence Laboratories. oai_citation:6‡md.silencelaboratories.com

What is technically concrete vs aspirational Concrete: MPC as the default engine, with references to classical MPC families (garbled circuits, BGW, GMW), HE and ZK for specific tasks, and a claim of production-tested MPC infra. Aspirational: a generalized “VM” abstraction that seamlessly composes across all A2A partners and workloads, with compliance guarantees that map cleanly to varied regs. Silence Laboratories. oai_citation:7‡md.silencelaboratories.com

Fit with A2A and your agent stack The design wraps A2A handshakes with a compute-privacy capsule, keeps prompts, embeddings, and user secrets opaque across orgs, and anticipates MCP-style tool interfaces behind each agent. If you run enclave-first pipelines, you can treat CCVM as a cryptography-first adjunct or fallback when TEEs are not mutually trusted. Silence Laboratories. oai_citation:8‡md.silencelaboratories.com

Red flags and open questions to send the vendor

  1. Threat model clarity: which party you must trust, what the collusion assumptions are, and whether semi-honest vs malicious adversaries are supported across protocols.
  2. Side-channels: do timing, transcript sizes, and adaptive prompts leak business signals. Any padding or ORAM-like mitigations.
  3. Performance proofs: end-to-end latencies for realistic A2A tasks, not just threshold-sig numbers. Include bandwidth and concurrency curves.
  4. Key and consent lifecycle: how consent is bound to keys or policies, revocation semantics, and auditable receipts that travel with outputs.
  5. ZK scope: which compliance assertions are proven, against what public inputs, and how verifiers validate at agent speed.
  6. Failure safety: atomicity of multi-party tasks, rollback, dispute resolution, and replay protections on A2A channels.
  7. Interop: how CCVM negotiates protocol choice (MPC vs HE) and parameters across heterogeneous partners without custom glue.
  8. Developer ergonomics: SDKs, typed policies, and how “minimal output” is specified to avoid silent privacy regressions.
    Silence Laboratories. oai_citation:9‡md.silencelaboratories.com

Quick next steps you can act on • Ask for a demo that runs private set intersection plus a small model inference across two orgs, with live metrics and audit artifacts.
• Request a short security whitebox that maps threat models to concrete protocol choices per use case.
• Pilot on synthetic or de-risked slices, then scale with formal data cards and consent receipts.
Silence Laboratories. oai_citation:10‡md.silencelaboratories.com #+END_SRC