Youyang Qu’s research while affiliated with The Commonwealth Scientific and Industrial Research Organisation and other places

underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M obile E dge C omputing (MEC) is an emerging technology, where App vendors are allowed to cache multiple data replicas on geographically distributed edge servers to serve adjacent mobile subscribers. However, this benefit introduces an extra workload for edge servers and App vendors, as they must audit the integrity of multiple data replicas periodically considering various threats caused by distributed and dynamic MEC environments. The large-scale growth of data replicas certainly is a challenge to design more efficient E dge D ata I ntegrity (EDI) verification approaches. Existing solutions are mostly limited to improving efficiency by optimizing proof generation and verification methods, while the improvement is still far from satisfactory due to adopting indiscriminate inspection philosophy (checking all data replicas without discrimination). In this paper, we make the first attempt to abstract a pre-processing phase and correspondingly study the U nreliable data R eplica S election (URS) problem. It can be seamlessly integrated into existing EDI solutions by solving the URS problem at the start of each verification round. Such pre-selection can significantly enhance overall EDI verification efficiency by incorporating the cache service Q uality o f S ervice (QoS) and verification success rate, especially in scenarios with a large number of data replicas. Specifically, we first formalize the URS problem as a constrained optimization problem, and further prove its $\mathcal {NP}$ -hardness. To address the problem efficiently, we transform it into an easy-to-handle form and develop a P riority-based approach named URS-P. Both theoretical analysis and experimental evaluation validate the effectiveness and efficiency of our proposed solution.

Many real-world interconnections among entities can be characterized as graphs. Collecting local graph information with balanced privacy and data utility has garnered notable interest recently. This paper delves into the problem of identifying and protecting critical information of entity connections for individual participants in a graph based on cohesive subgraph searches. This problem has not been addressed in the literature. To address the problem, we propose to extract the critical connections of a queried vertex using a fortress-like cohesive subgraph model known as p -cohesion. A user's connections within a fortress are obfuscated when being released, to protect critical information about the user. Novel merit and penalty score functions are designed to measure each participant's critical connections in the minimal p -cohesion., facilitating effective identification of the connections. We further propose to preserve the privacy of a vertex enquired by only protecting its critical connections when responding to queries raised by data collectors. We prove that, under the decentralized differential privacy (DDP) mechanism, one's response satisfies $(\varepsilon , \delta )$ -DDP when its critical connections are protected while the rest remains unperturbed. The effectiveness of our proposed method is demonstrated through extensive experiments on real-life graph datasets.

The vision for 6G aims to enhance network capabilities with faster data rates, near-zero latency, and higher capacity, supporting more connected devices and seamless experiences within an intelligent digital ecosystem where artificial intelligence (AI) plays a crucial role in network management and data analysis. This advancement seeks to enable immersive mixed-reality experiences, holographic communications, and smart city infrastructures. However, the expansion of 6G raises critical security and privacy concerns, such as unauthorized access and data breaches. This is due to the increased integration of IoT devices, edge computing, and AI-driven analytics. This paper provides a comprehensive overview of 6G protocols, focusing on security and privacy, identifying risks, and presenting mitigation strategies. The survey examines current risk assessment frameworks and advocates for tailored 6G solutions. We further discuss industry visions, government projects, and standardization efforts to balance technological innovation with robust security and privacy measures.

Many real-world interconnections among entities can be characterized as graphs. Collecting local graph information with balanced privacy and data utility has garnered notable interest recently. This paper delves into the problem of identifying and protecting critical information of entity connections for individual participants in a graph based on cohesive subgraph searches. This problem has not been addressed in the literature. To address the problem, we propose to extract the critical connections of a queried vertex using a fortress-like cohesive subgraph model known as p-cohesion. A user's connections within a fortress are obfuscated when being released, to protect critical information about the user. Novel merit and penalty score functions are designed to measure each participant's critical connections in the minimal p-cohesion, facilitating effective identification of the connections. We further propose to preserve the privacy of a vertex enquired by only protecting its critical connections when responding to queries raised by data collectors. We prove that, under the decentralized differential privacy (DDP) mechanism, one's response satisfies $(\varepsilon, \delta)$-DDP when its critical connections are protected while the rest remains unperturbed. The effectiveness of our proposed method is demonstrated through extensive experiments on real-life graph datasets.

This article presents a comprehensive review of recent machine unlearning techniques, verification mechanisms, and potential attacks. We highlight emerging challenges and prospective research directions, aiming to provide valuable resources for integrating privacy, equity, and resilience into machine learning systems and help them “learn to unlearn.”

In the realm of music composition, sentiment plays a pivotal role in connecting compositions with their audience, evoking emotions and memories. With the rapid evolution of artificial intelligence (AI), there exists a burgeoning interest in utilizing AI for sentiment analysis in various domains, including textual data, social media, and film. This paper delves into the novel application of AI-driven sentiment analysis specifically tailored for music composition. Leveraging diverse music datasets across multiple genres and eras, we introduce an innovative methodology that breaks down music into foundational features such as melody, rhythm, timbre, and harmony. Through the application of advanced AI techniques, including neural networks and Long Short-Term Memory (LSTM) models, we aim to accurately map these features to a wide spectrum of sentiments. Our results showcase not only the potential accuracy and precision of our chosen models but also the richness of music compositions they can produce, underscoring the viability of AI in enhancing the emotional depth of musical works. The implications of this research stretch from aiding composers in creating more resonant pieces to the potential therapeutic applications of AI-composed music, tailored to specific emotional needs.

The field of music composition has seen significant advancements with the introduction of artificial intelligence (AI) techniques. However, traditional cloud-based approaches suffer from limitations such as latency and network dependency. This survey paper explores the emerging concept of edge intelligence and its application in music composition. Edge intelligence leverages local computational resources to enable real-time and on-device music generation, enhancing the creative process and expanding accessibility. By examining various aspects of music composition, including melody creation, harmonization, rhythm generation, arrangement and orchestration, and lyric writing, this paper showcases the potential benefits of incorporating edge intelligence. It also discusses the challenges and limitations associated with this paradigm, such as limited computational resources and privacy concerns. Through a review of existing AI-based music composition tools and platforms, examples of edge intelligence in action are highlighted. The survey paper concludes by emphasizing the transformative potential of edge intelligence in revolutionizing the field of music composition and identifies future research opportunities to further advance this promising domain.