Fine-Tuning Pretrained Models for Privacy-Preserving Machine Learning

Strategic Insights -

Researchers at Georgia State University have made significant breakthroughs in fine-tuning pretrained models for privacy-preserving machine learning, addressing challenges related to data sharing due to stringent privacy regulations or user apprehension. By introducing a system called BlindTuner, the researchers enabled privacy-preserving fine-tuning by training data-efficient image transformers directly on homomorphically encrypted data. This innovation has the potential to facilitate collaborative learning without compromising privacy. The study has been peer-reviewed and published in the Ieee Internet of Things Journal.

Key Takeaways:

  • BlindTuner is a system that enables privacy-preserving fine-tuning by training data-efficient image transformers directly on homomorphically encrypted data.
  • The system achieves accuracy comparable to unencrypted models while delivering training speedups between 1.16x and 600x over existing approaches.
  • BlindTuner performs privacy-preserving inference, extending its usability beyond training.
  • The research explores the application of BlindTuner in privacy-preserving federated fine-tuning, enabling solution for collaborative learning without compromising privacy.
  • The study focuses on addressing the limitations of existing approaches, including efficiency and accuracy tradeoffs in privacy-preserving transfer learning.
  • The researchers, Prajwal Panzade, Javad Rafiei Asl, Daniel Takabi, and Zhipeng Cai, have made significant contributions to the field of privacy-preserving machine learning.
  • The National Science Foundation provided financial support for this research.

Statistics:

  • Training speedups achieved by BlindTuner range from 1.16x to 600x over existing approaches.
  • Accuracy comparable to unencrypted models achieved by BlindTuner.
  • 12.19 (volume and issue number) of the Ieee Internet of Things Journal published the research.
  • Ieee Internet of Things Journal is published by Ieee-inst Electrical Electronics Engineers Inc.

Sources:

  • Ieee Internet of Things Journal
  • NewsRx: Findings from Georgia State University Provides New Data about Information and Data Encoding and Encryption (Blindtuner: On Enhancement of Privacy-preserving Fine-tuning of Transformers Based On Homomorphic Encryption).
  • NewsRx: Information Technology Newsweekly, October 21, 2025, p 198.