Memory Efficient Privacy-Preserving Machine Learning Based on Homomorphic Encryption

Abstract

Fully Homomorphic Encryption (FHE) enables computation on encrypted data and can be used to provide privacy-preserving computation for machine learning models. However, FHE is computationally expensive and requires significant memory. Single instruction multiple data (SIMD) can offset this cost. Batch-packing, an SIMD technique that packs data along the batch dimension, requires significant memory. In convolutional neural networks, we can exploit their regular and repeating structure to reduce the memory cost by caching recurring values. In this paper, we investigate strategies for dynamically loading data from persistent storage and how to cache it effectively. We propose a method that reorders operations inside the convectional layer to increase caching effectiveness and reduce memory requirements. We achieve up to 50x reduction in memory requirements with only a 13% increase in runtime compared to keeping the data in memory during the entire computation. Our method is up to 38% faster at no significant memory difference compared to not using caching. We also show that our approach is up to 4.5x faster than the operating system’s swapping technique. These improvements allow us to run the models on less powerful and cheaper hardware.