Authenticated key exchange (AKE) allows two parties to authenticate each other and establish a secret session key to build a secure channel, and it has been well studied in the literature. With the approaching of quantum computers, designing post-quantum secure AKE schemes becomes an urgent task. Today, several KE schemes have been proposed while only a small number of AKE schemes exist. Very few of them have considered resource-constrained participants like IoT devices. In this paper, assuming a typical client-server setting where the client is an IoT device, we propose a modular framework that converts any post-quantum secure KE scheme into a post-quantum secure AKE scheme. Equipped with two authentication factors, the resulted AKE scheme provides a number of security guarantees including Perfect forward secrecy (PFS), Key compromise impersonation (KCI) resilience and Server compromise impersonation (SCI) resilience. We further instantiate the framework by selecting the most efficient KE scheme, namely NewHope Compact, and implement the scheme with some optimisation techniques and conduct relevant analysis and comparisons. In a nutshell, the computational time of the server side is 1.11 ms on a PC and 6.22 ms of the IoT device simulated on Raspberry Pi 3B+, and it seems to be efficient enough for most IoT application scenarios.

Singular value decomposition (SVD) is a fundamental technique widely used in various applications, such as recommendation systems and principal component analyses. In recent years, the need for privacy-preserving computations has been increasing constantly, which concerns SVD as well. Federated SVD has emerged as a promising approach that enables collaborative SVD computation without sharing raw data. However, existing federated approaches still need improvements regarding privacy guarantees and utility preservation. This paper moves a step further towards these directions: we propose two enhanced federated SVD schemes focusing on utility and privacy, respectively. Using a recommendation system use-case with real-world data, we demonstrate that our schemes outperform the state-of-the-art federated SVD solution. Our utility-enhanced scheme (utilizing secure aggregation) improves the final utility and the convergence speed by more than 2.5 times compared with the existing state-of-the-art approach. In contrast, our privacy-enhancing scheme (utilizing differential privacy) provides more robust privacy protection while improving the same aspect by more than 25%.