BREAKING: A groundbreaking study has just been released, revealing an efficient privacy-preserving scheme for secure neural network inference. Researchers from Southeast University and Purple Mountain Laboratories have developed a method that addresses critical privacy and security vulnerabilities in cloud computing.
As smart devices proliferate, users frequently transfer sensitive data to cloud servers for processing. However, this practice raises alarming privacy concerns as cloud servers may access user data without permission. The new scheme aims to protect both user data and server models, ensuring that private information remains confidential during neural network inference.
The research, titled “Efficient Privacy-Preserving Scheme for Secure Neural Network Inference,” utilizes advanced technologies like homomorphic encryption and secure multi-party computation. This approach allows for fast and accurate inference while safeguarding user privacy.
Key features of the scheme include:
1. **Three-Stage Inference**: The process is divided into merging, preprocessing, and online stages to enhance efficiency.
2. **Network Parameter Merging**: This innovative method reduces multiplication levels and minimizes ciphertext-plaintext operations.
3. **Fast Convolution Algorithm**: This algorithm boosts computational efficiency by transforming convolutional operations into matrix-vector multiplications.
Leveraging the CKKS homomorphic encryption algorithm, this scheme achieves impressive results. In extensive tests on the MNIST and Fashion-MNIST datasets, it recorded an accuracy of 99.24% and 90.26% respectively.
Compared to existing methods such as DELPHI, GAZELLE, and CryptoNets, the new scheme significantly enhances performance. It reduces online-stage linear operation time by at least 11%, cuts online-stage computational time by approximately 48%, and decreases communication overhead by 66% compared to non-merging approaches.
The implications of this research are enormous. It not only propels the field of secure neural networks forward but also provides a robust solution to the pressing issue of data privacy in cloud computing. As more industries rely on cloud services, this innovation is essential for maintaining user trust and confidence.
The research paper is authored by Liquan CHEN, Zixuan YANG, Peng ZHANG, and Yang MA. For further details, the full text of the paper can be accessed at: https://doi.org/10.1631/FITEE.2400371.
Stay tuned for more updates on this significant development, which is poised to change the landscape of data privacy and cloud security.
