Enhanced privacy-preserving distributed deep learning with application to fog-based IoT

Abstract

Generally, privacy-preserving distributed deep learning (PPDDL) solutions provided so far present a trade-off between privacy and efficiency/effectiveness, especially, in terms of high communication and run-time costs (i.e. efficiency) and declined accuracy (i.e. effectiveness). Additionally, not much attention has been paid to proposing privacy-preserving solutions that are multi-key compliant, able to extend to new participants and well fit for an unbounded number of participants as well as being post-quantum robust. Also, application of DDL to fog-based IoT needs more attention. To this effect, we present enhanced PPDDL by proposing two solutions (basic and advanced). The basic combines LWE-based additive homomorphic encryption and partial sharing, making it communication cost friendly. The advanced, however, uses a lattice-based fully dynamic multi-key fully homomorphic encryption (FHE) scheme and partial sharing with the additional advantages of being multi-key compliant, able to extend to new participants and well fit for an unbounded number of participants. Furthermore, we put our work in the context of Fog-Based IoT. Extensive experimental evaluations for the basic solution which also best suits the Fog-Based IoT, show that the PPDDL accuracy is maintained, recording accuracy of about 97% for the MNIST dataset. For a much deeper architecture, accuracy could reach about 99%. To assess the generalizability of the accuracy trend observed in the PPDDL, the proposed solution was also evaluated using the CIFAR-10 dataset with interesting results recorded in this study. This paper is innovative as it combines partial sharing and homomorphic encryption in DDL . Solutions are post-quantum robust and communication cost efficient.