Decentralised Trust Layers for the Web: Towards Transparent AI-Powered Platforms
Transparent AI for the Enterprise
This paper proposes a Decentralised Trust Layer (DTL) to enhance transparency and trustworthiness in AI-powered web platforms. DTL aims to provide independent verification of AI behavior and data provenance by turning transparency from policy into protocol, using cryptographic commitments, append-only logs, and verifiable identities. It's designed as a modular sidecar microservice, comprising four layers: provenance anchoring, a model registry, an inference transparency log, and governance/auditing mechanisms. The evaluation focuses on cryptographic and logging overheads, showing microsecond-scale per-request costs and logarithmic proof sizes, indicating practical deployability for interactive web services.
Key Impacts & Metrics
Our analysis reveals the transformative potential of DTL across critical enterprise functions.
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Increase in Trust
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Faster Audits
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Bias Reduction
Deep Analysis & Enterprise Applications
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System Architecture
Threat Model & Security
Evaluation & Performance
DTL is a modular trust substrate deployed as a sidecar to AI services, providing tamper-evident provenance, a model registry, inference transparency via append-only logging, and decentralised governance. It adapts transparency-log patterns from Web PKI and aligns with W3C standards.
The DTL framework addresses threats such as omitting/rewriting decision logs, wrong model versions, substituted provenance, split-view equivocation, privacy leakage, and Sybil auditor influence, primarily through cryptographic accountability, append-only verifiable history, and selective disclosure.
Evaluation focuses on cryptographic overheads and proof verification costs. Results show microsecond-scale per-request costs for receipt generation and logarithmic proof sizes, demonstrating practical deployability with minimal impact on serving latency.
Microsecond-Scale Overhead
Receipt generation and Merkle batching incur microsecond-scale overhead, ensuring DTL's viability for high-throughput web services without significant latency impact.
6.2 µs µs/receipt (mean)Enterprise Process Flow
User/Client Request
→
AI Platform Model Serving
→
DTL Sidecar Microservice
→
Append-only Anchor/Ledger
→
Auditors/Verifiers
| Approach | Non-equiv. log | Model bind. | Policy/decision bind. | Sidecar deployable | Auditor receipts/proofs | Privacy-by-default |
|---|---|---|---|---|---|---|
| PROV / model cards / datasheets | ~ | |||||
| Certificate Transparency-style logs | ✓ | ✓ | ||||
| in-toto / SLSA attestations | ✓ | ✓ | ||||
| DTL (this work) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Proof Scaling & Verification
Proof sizes grow linearly in the number of levels while remaining compact (hundreds of bytes) even for large logs. Verification time grows modestly with depth, consistent with the small number of hash operations required per proof. These trends imply that third-party auditing can remain lightweight: auditors can verify receipts at scale without needing access to platform internals, and proof transmission overhead stays small relative to typical web payload sizes. For a log with 2^16 leaves (65536), the proof size is 512 bytes and verification time is ~18 µs.
Enhancing Web Trust with DTL
DTL provides a practical path towards a more trustworthy AI-mediated Web by combining provenance anchoring, version-bound model metadata, transparency-log-style inference receipts, and decentralised auditing.
Real-world Impact
DTL’s approach supports user-centric accountability and provides a foundation upon which regulators, researchers, and platforms can build credible assurance processes as AI continues to reshape the digital public sphere.
- ✓ Verifiable commitments, not just narrative claims
- ✓ Independent auditing without privileged platform access
- ✓ Compliance with W3C identity and provenance standards
- ✓ Scalable and privacy-preserving mechanisms
Advanced ROI Calculator
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Implementation Roadmap
A phased approach to integrating Decentralised Trust Layers into your AI infrastructure.
Phase 1: Receipt Engine Integration
Implement hashing, signing, and commitment creation modules as a sidecar. Focus on minimizing per-request overhead.
Phase 2: Log Engine & Registry
Integrate Merkle batching, root signing, proof generation, and connect to model registries for fetching and verifying model bundles.
Phase 3: Decentralized Governance
Establish mechanisms for auditor admission, dispute resolution, and policy enforcement via smart contracts or consortium policies.
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