New post-quantum Key Encapsulation Mechanism (KEM) designs, evaluated as part of the NIST PQC standardization Project, pose challenging tradeoffs between communication bandwidth and computational overheads. Several KEM designs evaluated in Round-2 of the project are based on QC-MDPC codes. BIKE-2 uses the smallest communication bandwidth, but its key generation requires a costly polynomial inversion. In this paper, we provide details on the optimized polynomial inversion algorithm for QC-MDPC codes (originally proposed in the conference version of this work). This algorithm makes the runtime of BIKE-2 key generation tolerable. It brings a speedup of 11.4× over the commonly used NTL library, and 83.5× over OpenSSL. We achieve additional speedups by leveraging the latest Intel's Vector-PCLMULQDQ instructions, 14.3× over NTL and 103.9× over OpenSSL. Our algorithm and implementation were the reason that BIKE team chose BIKE-2 as the only scheme for its Round-3 specification (now called BIKE).

This paper demonstrates the use of new processor instructions VPMADD, intended to appear in the coming generation of Intel processors (codename "Cannon Lake"), in order to accelerate the newly proposed key encapsulation mechanism (KEM) named SIKE. SIKE is one of the submissions to the NIST standardization process on post-quantum cryptography, and is based on pseudo-random walks in supersingular isogeny graphs. While very small keys are the main advantage of SIKE, its extreme computational intensiveness makes it one of the slowest KEM proposals. Performance optimizations are needed. We address here the "Level 1" parameters that target 64-bit quantum security, and deemed sufficient for the NIST standardization effort. Thus, we focus on SIKE503 that operates over Fp2 with a 503-bit prime p. These short operands pose a significant challenge on using VPMADD effectively. We demonstrate several optimization methods to accelerate Fp, Fp2, and the elliptic curve arithmetic, and predict a potential speedup by a factor of 1.72x.

In this paper we present a novel approach for dynamic remote activity monitoring based on mobile complex event processing that has been used in a use case in the eHealth domain. Using complex event processing (CEP) in mobile environment enables a more flexible and efficient processing of personal sensor data and environment data, which are detected by sensors embedded in a mobile device. The main advantages of our approach are: an efficient combination of the mobile and server-side event processing through the semantic event model, optimal usage of mobile resources through dynamic management of mobile event processing and modelling of complex situations by using more expressive knowledge representation formalism. We present the settings for the use case and the results from the preliminary evaluation.