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Randomness is integral to computer security, influencing fields such as cryptography and machine learning. In the context of cybersecurity, particularly for the Internet of Things (IoT), high levels of randomness are essential to secure cryptographic protocols. Quantum computing introduces significant risks to traditional encryption methods. To address these challenges, we propose investigating a quantum-safe solution for IoT-trusted computing. Specifically, we implement the first lightweight, practical integration of a quantum random number generator (QRNG) with a software-based trusted platform module (TPM) to create a deployable quantum trusted platform module (QTPM) prototype for IoT systems to improve cryptographic capabilities. The proposed quantum entropy as a service (QEaaS) framework further extends quantum entropy access to legacy and resource-constrained devices. Through the evaluation, we compare the performance of QRNG with traditional Pseudo-random Number Generators (PRNGs), demonstrating the effectiveness of the quantum TPM. Our paper highlights the transformative potential of integrating quantum technology to bolster IoT security. 

This article presents a novel network protocol that incorporates a quantum photonic channel for symmetric key distribution, a Dilithium signature to replace factor-based public key cryptography for enhanced authentication, security, and privacy. The protocol uses strong hash functions to hash original messages and verify heightened data integrity at the destination. This Quantum good authentication protocol (QGP) provides high-level security provided by the theory of quantum mechanics. QGP also has the advantage of quantum-resistant data protection that prevents current digital computer and future quantum computer attacks. QGP transforms the transmission control protocol/internet protocol (TCP/IP) by adding a quantum layer at the bottom of the Open Systems Interconnection (OSI) model (layer 0) and modifying the top layer (layer 7) with Dilithium signatures, thus improving the security of the original OSI model. In addition, QGP incorporates strong encryption, hardware-based quantum channels, post-quantum signatures, and secure hash algorithms over a platform of decryptors, switches, routers, and network controllers to form a testbed of the next-generation, secure quantum internet. The experiments presented here show that QGP provides secure authentication and improved security and privacy and can be adopted as a new protocol for the next-generation quantum internet. 

In this study, we apply machine learning and software engineering in analyzing air pollution levels in City of Baltimore. The data model was fed with three primary data sources: 1) a biased method of estimating insurance risk used by homeowners loan corporation, 2) demographics of Baltimore residents, and 3) census data estimate of NO2 and PM2.5 concentrations. The dataset covers 650,643 Baltimore residents in 44.7 million residents in 202 major cities in US. The results show that air pollution levels have a clear association with the biased insurance estimating method. Great disparities present in NO2 level between more desirable and low income blocks. Similar disparities exist in air pollution level between residents' ethnicity. As Baltimore population consists of a greater proportion of people of color, the finding reveals how decades old policies has continued to discriminate and affect quality of life of Baltimore citizens today. A QML-based feature mapping is applied on a small dataset.