HETAL: New Privacy-Preserving Method for Transfer Learning with Homomorphic Encryption

 HETAL: New Privacy-Preserving Method for Transfer Learning with Homomorphic Encryption

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Data Privacy and Machine Learning

Data privacy is a significant concern in today’s world, with laws like the EU’s General Data Protection Regulation (GDPR) aiming to safeguard personal information. When using pre-trained models in machine learning, sharing data features with model providers can pose privacy risks.

Introducing HETAL: Privacy-Preserving Transfer Learning

A team of researchers has developed HETAL, an efficient algorithm using homomorphic encryption (HE) for privacy-preserving transfer learning. The method allows clients to encrypt data features and send them to a server for fine-tuning without compromising data privacy.

Efficiency and Effectiveness of HETAL

HETAL optimizes encrypted matrix multiplications and introduces novel algorithms that significantly reduce computational costs. Experiments on benchmark datasets showed that HETAL’s encrypted models achieved high accuracy while maintaining practical runtimes.

Practical Applications and Value

HETAL addresses a crucial challenge in privacy-preserving machine learning and represents a significant step towards practical, privacy-preserving solutions for machine learning as a service. The proposed method has the potential to benefit other applications involving neural networks and encrypted computations.

For more information, you can check out the Paper and Github.

AI Solutions for Your Company

If you want to evolve your company with AI, consider integrating HETAL for privacy-preserving transfer learning with homomorphic encryption.

Practical Steps for AI Integration

  • Identify Automation Opportunities
  • Define KPIs
  • Select an AI Solution
  • Implement Gradually

For AI KPI management advice and continuous insights into leveraging AI, connect with us at hello@itinai.com, or stay tuned on our Telegram or Twitter.

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