A platform for practical homomorphic encryption in neural network classification : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy (Ph.D.) in Information Technology, Massey University
Convolutional neural networks (CNN) have become remarkably better in correctly identifying and classifying objects. By using CNN, numerous online services now exist that processes our data to provide meaningful insight and value-added services. Not all services are reliable and trustworthy due to which privacy concerns exist. To address the issue, the work presented in this research develops and optimise new techniques to use Homomorphic Encryption (HE) as a solution. Researchers have proposed solutions like the CryptoNets, Gazelle, and CryptoDL. However, homomorphic encryption is yet to see the limelight for real-world adoption, especially in neural networks. These proposed solutions are seen as a solution only for a particular CNN model and lack generality to be extended to a different CNN model. Moreover, the solutions for HE-CNN integration are seen as unprepared for adoption in a practical and real-world environment. Additionally, the complex integration of hybrid approaches limits their utilization with privacy-preserving based CNN models. For that reason, this research develops the mathematical and practical knowledge required to adopt HE within a CNN. This knowledge of performing encrypted classification for a CNN model is based on a careful selection of appropriate encryption parameters. Furthermore, this study succeeds in developing a dual-cloud system to mitigate many of the technical hurdles for evaluating an encrypted neural network without compromising privacy. Moreover, in the case of a single cloud, this study develops methods for overcoming technical issues in selecting encryption parameters for, and evaluating, a convolutional neural network. In the same context, the novel method of selecting and optimizing encryption parameters based on probability is given. The proposals and the knowledge from this research can aid and advance the strategies of HE-CNN integrations in an efficient and easy way.