Distributed quantum learning (DQL) has emerged as a promising paradigm to scale quantum-enhanced machine learning by interconnecting multiple quantum devices. However, for efficient real-world deployment, it is essential to characterize how DQL converges under practical scenarios while simultaneously safeguarding multi-device quantum infrastructures from evolving security threats. Addressing these aspects in an integrated manner is key to ensuring both performance and resilience in large-scale DQL systems. Therefore, this paper presents a new DQL study where our innovation lies in: (i) conducting a holistic convergence analysis for DQL under practical settings, i.e., partial device participation, non-convex loss functions, and heterogeneous data distributions, (ii) developing a novel multi-layered post-quantum cryptographic architecture with a quantum neural network-powered adaptive mechanism that monitors conditions, evaluates threats, and adjusts parameters across three National Institute of Standards and Technology (NIST)-compliant levels. Our theoretical framework and empirical validation reveal two key insights: (i) the derived convergence bound uncovers a fundamental trade-off between convergence rate, measurement shots, and the size of the participating device subset; and (ii) findings from our evaluations on a physical testbed modeling quantum control architectures expose the performance limitations of static post-quantum security, while confirming that our adaptive framework effectively mitigates these overheads to preserve overall system efficiency. Specifically, the hardware experiments demonstrate that our dynamic security mechanism reduces total security execution time by approximately 49% relative to static high-security baselines, while maintaining a threat detection accuracy of over 91%. Furthermore, extensive simulations validate our theoretical analysis, showing strong agreement between predicted and observed convergence trends. The coupling of these two stages ensures both theoretically grounded performance optimization and adaptive threat resilience, enabling efficient, secure, and scalable DQL deployment.

Long-range wide-area network (LoRaWAN) is a low-power wide-area network (LP-WAN) protocol developed for low-bandwidth, battery-operated long-range sensors. However, the LoRaWAN specification has several security issues and utilizes software cryptography, which is not energy efficient. Our proposed solution combines a modified LoRaWAN protocol with a hardware-based cryptographic coprocessor that includes secure on-chip key storage for encryption, decryption, and digital signature creation. This design reduces the energy utilization of the wireless nodes while addressing several of the LoRaWAN threat surfaces. This article provides a security analysis of the reduced threat surfaces and demonstrates the improved energy efficiency of the LoRaWAN nodes with the integrated solution.

Visualization and situational awareness are of vital importance for power systems, as the earlier a power-system event such as a transmission line fault or cyber-attack is identified, the quicker operators can react to avoid unnecessary loss. Accurate time-synchronized data, such as system measurements and device status, provide benefits for system state monitoring. However, the time-domain analysis of such heterogeneous data to extract patterns is difficult due to the existence of transient phenomena in the analyzed measurement waveforms. This paper proposes a sequential pattern mining approach to accurately extract patterns of power-system disturbances and cyber-attacks from heterogeneous time-synchronized data, including synchrophasor measurements, relay logs, and network event monitor logs. The term common path is introduced. A common path is a sequence of critical system states in temporal order that represent individual types of disturbances and cyber-attacks. Common paths are unique signatures for each observed event type. They can be compared to observed system states for classification. In this paper, the process of automatically discovering common paths from labeled data logs is introduced. An included case study uses the common path-mining algorithm to learn common paths from a fusion of heterogeneous synchrophasor data and system logs for three types of disturbances (in terms of faults) and three types of cyber-attacks, which are similar to or mimic faults. The case study demonstrates the algorithm's effectiveness at identifying unique paths for each type of event and the accompanying classifier's ability to accurately discern each type of event.

Abstract As cellular telephones and high capacity memory sticks emerge as users' primary repository for data and applications, users will often run applications and display data on remote hosts. The biggest challenge in supporting this mobile data, mobile applications, stationary platform model is ensuring the security and privacy of user applications and data during execution on the remote platforms. Private computing on public platforms (PCPP) is a new application security approach which isolates applications to allow for secure and private execution on third party systems. This paper introduces PCPP and details its five basic building blocks which together ensure that the PCPP protected application's executable code, context, and data remain unaltered, unmonitored, and unrecorded before, during, and after exposure to the remote platform. Copyright © 2009 John Wiley & Sons, Ltd.

Private computing on public platforms (PCPP) is a new technology designed to enable secure and private execution of applications on remote potentially hostile public platforms. PCPP uses a host assessment to validate a host's hardware and software configuration and then uses 4 active security building blocks which together allow an application to remain unaltered, unmonitored, and unrecorded before, during, and after execution on the public platform. Privacy protected ELF (PPELF) is the building block used by PCPP to protect application executable code while it is stored on the public platform and to provide a secure binary load mechanism. PPELF is an encrypted binary executable format and methodology which uses just in time decryption during the executable load procedure. In this paper we describe the PPELF file format, our new PPELF binary format loader, and our updates to the GLIBC ELF interpreter to support PPELF. We also provide experimental results detailing the initial load-time and run-time penalty associated with the use of PPELF.

Private computing on public platforms (PCPP) is a new technology designed to enable secure and private execution of applications on remote, potentially hostile, public platforms. PCPP uses a host assessment to validate a host's hardware and software configuration and then uses four active security building blocks which together allow an application to remain unaltered, unmonitored, and unrecorded before, during, and after execution on the public platform. In this paper we develop a key PCPP building block, secure context switch (SCS), which isolates an executing application's context, i.e. its executable code, data segments, heap, and stack, using encryption techniques. Additionally, we detail our implementation of SCS and offer experimental results showing the performance impact of protecting an application with SCS.

Private computing on a public platform (PCPP) is a new paradigm in public computing in which an application executes on a previously unknown remote system securely and privately. The first step in the PCPP process is remote assessment of a prospective remote host to determine whether it is capable of executing the PCPP application and to classify the host as a potential threat or non-threat. This paper explores the use of a naive Bayesian classifier to classify prospective remote hosts. We show that the naive Bayesian classifier learns to recognize subtle patterns in historical host measurements and performs the classification task accurately and with minimal negative performance implications.