A Programming API Implementation for Secure Data Analytics Applications with Homomorphic Encryption on GPUs

As sensitive data is frequently stored and processed in environments that are either shared, untrusted or otherwise can be compromised, privacy is frequently a concern. To address this, a method that has been gaining popularity is to use Homomorphic Encryption (HE), which allows computation over encrypted data, i.e., without decrypting the data first. However, the overhead of such analytics (up to 4 orders of magnitude) is a detriment and while there have been a few previous efforts on reducing these overheads through the use of accelerators like GPUs, programmability is a concern. This paper addresses both performance and programmability concerns with the use of HE. We port the major pieces of Simple Encrypted Arithmetic Library (SEAL) from Microsoft to GPU using CUDA. Through these GPU-based functions, a new HE application can be developed easily. We demonstrate this by developing encrypted versions of three applications - CNN, k-means, and KNN. The speedups of execution time for CNN, k-means and KNN on a single GPU over CPU implementation achieve up to 81, 133 and 7 respectively.