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Since 2016, the National Institute of Standards and Technology (NIST) has been performing a competition to standardize post-quantum cryptography (PQC). Although Falcon has been selected in the competition as one of the standard PQC algorithms because of its advantages in short key and signature sizes, its performance overhead is larger than that of other lattice-based cryptosystems. This study presents multiple methodologies to accelerate the performance of Falcon using graphics processing units (GPUs) for server-side use. Direct GPU porting significantly degrades performance because the Falcon reference codes require recursive functions in its sampling process. Thus, an iterative sampling approach for efficient parallel processing is presented. In this study, the Falcon software applied a fine-grained execution model and reported the optimal number of threads in a thread block. Moreover, the polynomial multiplication performance was optimized by parallelizing the number-theoretic transform (NTT)-based polynomial multiplication and the fast Fourier transform (FFT)-based multiplication. Furthermore, dummy-based parallel execution methods have been introduced to handle the thread divergence effects. The presented Falcon software on RTX 3090 NVIDA GPU based on the proposed methods with Falcon-512 and Falcon-1024 parameters outperform at 35.14, 28.84, and 34.64 times and 33.31, 27.45, and 34.40 times, respectively, better than the central processing unit (CPU) reference implementation using Advanced Vector Extensions 2 (AVX2) instructions on a Ryzen 9 5900X running at 3.7 GHz in key generation, signing, and verification, respectively. Therefore, the proposed Falcon software can be used in servers managing multiple concurrent clients for efficient certificate verification and be used as an outsourced key generation and signature generation server for Signature as a Service (SaS).

Since the COVID-19 pandemic, the medical field has actively attempted non-face-to-face transformations, and the use of the metaverse has attracted attention in this regard. In this study, we investigated and analyzed the user experience (UX) characteristics and inconveniences that appear in the service objects inside a counseling room at the Hallym University Medical Center's Metaverse Children's Burn Hospital, which was launched on the Gathertown metaverse platform. UX improvement of this hospital is expected to benefit other hospitals as well as non-face-to-face treatments that will eventually be launched in the Metaverse. We first analyzed the non-face-to-face treatment services on the Gathertown metaverse platform based on the techniques reported in the literature and using some examples. Second, the usability of the service object in the counseling room was analyzed via tests. Third, a major analysis was conducted on the UX of the service object. Fourth, a UX design of the service object was proposed, and its usability was evaluated. By analyzing the data collected, the design direction was derived based on three keywords: Object connectivity, object unity, object visibility. Based on these keywords, the object placement and graphic design direction were set to improve the UX of the service object within the counseling room. To better utilize the metaverse platform in non-face-to-face treatments, investigation of other service objects will be conducted as a follow-up study.