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Matthew Chou

Publications and source records attributed to Matthew Chou.

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Network Impact of Post-Quantum Certificate Chain sizes on Time to First Byte in TLS Deployments

Post-Quantum Cryptography (PQC) is a rapidly growing deployment challenge as cryptographically relevant quantum computers (CRQC) continue to advance, leaving traditional cryptographic algorithms used in X.509 vulnerable to attack. However, PQC introduces significant deployment challenges in real-world networks, with handshake sizes increasing from 5x to over 20x compared to classical algorithms. In this work, we evaluate the time to first byte (TTFB) under CDN-focused TLS conditions to characterize the latency cost of transitioning existing internet infrastructure to quantum-safe certificate schemes. We observe discrete increases in TTFB as certificate chain sizes exceed transport layer data flight limits. To isolate the impact of certificate chains, we evaluate both ECDSA and ML-DSA-based certificate schemes, generating similarly sized certificate chains through controlled addition of certificate extensions. We additionally examine how CDN properties such as session resumption, certificate size optimizations, and geographical distribution reduce latency penalties. We utilize Zeek-monitored TLS traffic through a High-Performance Computing System (NCSA) with terabyte network connectivity across the nation to quantify real-world session resumption rates. We compare CDN-driven size optimization with Merkle Tree Certificates (MTC) to examine how size reductions allow certificate chains to remain under the flight limit threshold. We find that MTC allows for 2x-3x increase in supportable certificate chain size, whereas CDN-based optimizations yield more limited reductions, supporting up to approximately 1.6x certificate chain size increase.

cs.CR

Human Perception of LLM-generated Text Content in Social Media Environments

Emerging technologies, particularly artificial intelligence (AI), and more specifically Large Language Models (LLMs) have provided malicious actors with powerful tools for manipulating digital discourse. LLMs have the potential to affect traditional forms of democratic engagements, such as voter choice, government surveys, or even online communication with regulators; since bots are capable of producing large quantities of credible text. To investigate the human perception of LLM-generated content, we recruited over 1,000 participants who then tried to differentiate bot from human posts in social media discussion threads. We found that humans perform poorly at identifying the true nature of user posts on social media. We also found patterns in how humans identify LLM-generated text content in social media discourse. Finally, we observed the Uncanny Valley effect in text dialogue in both user perception and identification. This indicates that despite humans being poor at the identification process, they can still sense discomfort when reading LLM-generated content.

cs.HC

Finding the Center and Centroid of a Graph with Multiple Sources

We consider the problem of finding a "fair" meeting place when S people want to get together. Specifically, we will consider the cases where a "fair" meeting place is defined to be either 1) a node on a graph that minimizes the maximum time/distance to each person or 2) a node on a graph that minimizes the sum of times/distances to each of the sources. In graph theory, these nodes are denoted as the center and centroid of a graph respectively. In this paper, we propose a novel solution for finding the center and centroid of a graph by using a multiple source alternating Dijkstra's Algorithm. Additionally, we introduce a stopping condition that significantly saves on time complexity without compromising the accuracy of the solution. The results of this paper are a low complexity algorithm that is optimal in computing the center of S sources among N nodes and a low complexity algorithm that is close to optimal for computing the centroid of S sources among N nodes.

cs.DM