CuFE: High Performance Privacy Preserving Support Vector Machine With Inner-Product Functional Encryption

Abstract

Privacy preservation is a sensitive and important issue in this ever-growing and highly-connected digital era. Functional encryption is a computation on encrypted data paradigm that allows users to retrieve the evaluation of a function on encrypted data without revealing the data, effectively protecting user's privacy. However, existing functional encryption implementations are still very time-consuming for practical deployment, especially when applied to machine learning applications that involve huge amount of data. In this paper, we present a high-performance implementation of inner-product functional encryption (IPFE) based on ring-learning with errors on graphics processing units. We execute a systematic investigation to select the best strategy for implementing number theoretic transform for different security levels, which is the most time-consuming operations in the IPFE scheme. We further propose novel techniques to parallelize the Gaussian sampling. Compared to the existing AVX2 implementation, our implementation on a RTX 2060 GPU achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$34.24\times$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$40.02\times$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$156.30\times$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$18.76\times$</tex-math></inline-formula> speed-up for <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Setup</monospace> , <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Encrypt</monospace> , <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KeyGen</monospace> and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Decrypt</monospace> respectively. Finally, we propose a fast privacy-preserving SVM to classify data securely using our GPU-accelerated IPFE scheme. On average, our implementation can classify one input with 591 support vectors in 688 ms ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&lt; 1$</tex-math></inline-formula> second), which is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$33.12\times$</tex-math></inline-formula> faster than the AVX2 version.