Under development, coming soon

OutDated

Privacy-First Machine Learning→

PRIVACY-FIRST MACHINE LEARNING

Train machine learning models on sensitive data without ever exposing the raw information.

  • Confidential Enclave Training

    All compute inside hardware-isolated enclave (SGX/Nitro).

  • Decentralized Storage

    Erasure-coded, censorship-resistant data slivers.

  • Custom Access Control Conditions

    Decentralized key gating via ACCs.

  • Prelisting Verification

    Verify quality without revealing sensitive data.

  • Solana Access Ledger

    Real-time, tamper-proof dataset access logs on Solana

Trust & Verification

Ensuring authenticated, audited data sources through our multi-step verification process

On-Chain DID

Email Verification

Domain Proof

Metadata Tagging

Verified Dataset

Unverified Data

Raw datasets from unknown sources

Verified Data

Authenticated datasets with provider credentials

OutDated Whitepaper

Pre-Listing Verification System

Discover Our Verification System

Our comprehensive whitepaper details OutDated's innovative approach to pre-listing verification, ensuring data integrity, privacy compliance, and quality assurance for all datasets before they enter the marketplace.

Learn about our multi-stage pipeline that includes automated integrity checks, schema validation, privacy sanitization, quality assessment, and ML-powered classification.

scikit-learn–style API for zero-trust ML

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from outdated import SecureModel
 
model = SecureModel()
model.fit("walrus://dataset_hash")

Try It Yourself

See how outDated securely trains a model on encrypted data without exposing the raw information.

Interactive API Demo

Click the button to simulate training a model in a Trusted Execution Environment.

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