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

**[Machine Learning News](https://daily.dev/sources/mlnews)** · 4 min read · 1 upvotes · 0 comments

## Summary

HETAL is an efficient homomorphic encryption-based algorithm for privacy-preserving transfer learning. It enables clients to encrypt data features and send them to a server for fine-tuning without compromising data privacy. HETAL's novel matrix multiplication algorithms and softmax approximation technique have potential applications in other areas involving neural networks and encrypted computations.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.marktechpost.com/2024/03/27/hetal-new-privacy-preserving-method-for-transfer-learning-with-homomorphic-encryption/>

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---

Tags: [#neural-networks](https://daily.dev/tags/neural-networks), [#transfer-learning](https://daily.dev/tags/transfer-learning)

[View this post on daily.dev](https://daily.dev/posts/hetal-new-privacy-preserving-method-for-transfer-learning-with-homomorphic-encryption-ru6iwlywa)

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