Saturday 22 March 2025
The quest for more efficient and private machine learning models has led researchers to explore the realm of quantized neural networks. These models compress data into fewer bits, reducing storage needs and computational costs while maintaining performance levels comparable to their full-precision counterparts.
To evaluate the privacy risks associated with these models, scientists have developed a novel methodology that assesses the security of quantized algorithms against membership inference attacks. In these attacks, an adversary tries to determine whether a specific data sample was used to train the model or not.
The research team employed a combination of theoretical and empirical approaches to investigate this problem. They started by deriving an asymptotic theoretical analysis of Membership Inference Security (MIS), which characterizes the privacy implications of quantized algorithm weights against powerful attacks.
To validate their theory, they conducted extensive experiments using synthetic datasets and real-world applications in molecular modeling. The results showed that while some quantizers performed better than others in terms of accuracy, all of them presented varying levels of membership inference vulnerability.
One key finding was that the least private quantizers tend to preserve most of the original model’s performance on classification tasks, but struggle with regression examples. This is because regression tasks require more precise predictions, making it harder for the models to accurately capture the target values.
The study also highlighted the trade-off between security and downstream performance. As the privacy of the model decreases, its accuracy improves, demonstrating that there is no clear winner in this balancing act.
In addition, the researchers explored how different embedders (pre-trained neural networks used as input features) affect the privacy of the quantized models. Surprisingly, they found little variation in the quantizers’ privacy rankings across various embedders.
To better understand the behavior of these models, the team analyzed the evolution of a key metric called Λk,k, which measures the maximum value of λk = Λk,k = limn→∞(δn 2 /δnk)2 (σnk)2. They discovered that the maximum value is consistently reached on low k values, supporting their hypothesis about the sampling strategy for estimating membership inference risk.
Overall, this research sheds light on the privacy risks associated with quantized neural networks and provides valuable insights for developers seeking to balance security and performance in machine learning applications.
Cite this article: “Quantized Neural Networks: Balancing Security and Performance in Machine Learning”, The Science Archive, 2025.
Quantized Neural Networks, Membership Inference Attacks, Machine Learning Privacy, Data Compression, Computational Costs, Security Risks, Asymptotic Analysis, Synthetic Datasets, Real-World Applications, Embedders.
