Image Classification using non-linear Support Vector Machines on Encrypted Data

In image processing, algorithms for object classification are typically based around machine learning. From the algorithm developer\u27s perspective, these can involve a considerable amount of effort and expertise to develop, which makes them commercially valuable. On the other hand, other parties may want to make use of these algorithms to classify their images, while protecting the privacy of their data. In this paper, we show how non-linear Support Vector Machines (SVMs) can be practically used for image classification on data encrypted with a Somewhat Homomorphic Encryption (SHE) scheme. Previous work has shown how an SVM with a linear kernel can be computed on encrypted data, but this only has limited applicability. By enabling SVMs with polynomial kernels, a much larger class of applications are possible with more accuracy in classification results

EPIC: Efficient Private Image Classification (or: Learning from the Masters)

Outsourcing an image classification task raises privacy concerns, both from the image provider\u27s perspective, who wishes to keep their images confidential, and from the classification algorithm provider\u27s perspective, who wishes to protect the intellectual property of their classifier. We propose EPIC, an efficient private image classification system based on support vector machine (SVM) learning, which is secure against malicious adversaries. The novelty of EPIC is that it builds upon transfer learning techniques known from the Machine Learning (ML) literature and minimizes the load on the privacy-preserving part. Our solution is based on Secure Multiparty Computation (MPC), it is 34 times faster than Gazelle (USENIX 2018) --the state-of-the-art in private image classification-- and it improves the total communication cost by 50 times, while achieving a 7\% higher accuracy on CIFAR-10 dataset. When benchmarked for performance, while maintaining the same CIFAR-10 accuracy as Gazelle, EPIC is 700 times faster and the communication cost is reduced by 500 times