Centralization of Internet-based services in few key players has been a topic of study in recent years. One of such services, the Domain Name System (DNS), is one of the pillars of the Internet, which allows users to access websites on the Internet through easy-to-remember domain names rather than complex numeric IP addresses. In this DNS context, the reliance on a small number of large DNS providers can lead to (a) risks of data breaches and disruption of service in the event of failures and (b) concerns about the digital sovereignty of countries regarding DNS hosting. As several essential services are provided through Electronic-Government (E-Gov), it is highly important to be able to measure the digital sovereignty of a nation and the impacts that the lack of such feature can bring to its citizens. This work approaches the issue of DNS concentration on the Internet by presenting a solution to measure DNS hosting centralization and digital sovereignty in different countries, such as Brazil, India, China, Russia, and South Africa. With the data obtained through these measurements, relevant questions are answered, such as which are the top-10 DNS providers, if there is DNS centralization, and how dependent countries are on such providers to manage domains using their country code Top-Level Domains (ccTLD). Future opportunities could investigate the impacts on sovereignty under the lens of other layers of the Open Systems Interconnection (OSI) Network Sovereignty representation model presented in this work.

In the surge of the digital era, the Metaverse, as a groundbreaking concept, has become a focal point in the technology sector. It is reshaping human work and life patterns, carving out a new realm of virtual and real interaction. However, the rapid development of the Metaverse brings along novel challenges in security and privacy. In this multifaceted and complex technological environment, data protection is of paramount importance. The innovative capabilities of high-end devices and functions in the Metaverse, owing to advanced integrated circuit technology, face unique threats from Side-Channel Analysis (SCA), potentially leading to breaches in user privacy. Addressing the issue of domain differences caused by different hardware devices, which impact the generalizability of the analysis model and the accuracy of analysis, this paper proposes a strategy of Portability Power Profiling Analysis (PPPA). Combining domain adaptation and deep learning techniques, it models and calibrates the domain differences between the profiling and target devices, enhancing the model’s adaptability in different device environments. Experiments show that our method can recover the correct key with as few as 389 power traces, effectively recovering keys across different devices. This paper underscores the effectiveness of cross-device SCA, focusing on the adaptability and robustness of analysis models in different hardware environments, thereby enhancing the security of user data privacy in the Metaverse environment.

Large language models (LLMs), with their billions to trillions of parameters, excel in natural language processing, machine translation, dialogue systems, and text summarization. These capabilities are increasingly pivotal in the Metaverse, where they can enhance virtual interactions and environments. However, their extensive use, particularly in the Metaverse’s immersive platforms, raises significant privacy concerns. This paper analyzes existing privacy issues in LLMs, vital for both traditional and Metaverse applications, and examines protection techniques across the entire life cycle of these models, from training to user deployment. We delve into cryptography, embedding layer encoding, differential privacy and its variants, and adversarial networks, highlighting their relevance in the Metaverse context. Specifically, we explore technologies like homomorphic encryption and secure multi-party computation, which are essential for Metaverse security. Our discussion on Gaussian differential privacy, Renyi differential privacy, Edgeworth accounting, and the generation of adversarial samples and loss functions, emphasizes their importance in the Metaverse’s dynamic and interactive environments. Lastly, the paper discusses the current research status and future challenges in the security of LLMs within and beyond the Metaverse, emphasizing urgent problems and potential areas for exploration.

Besides cryptocurrencies, DLTs may be also exploited in enterprise systems operated by a consortium of organizations. Their interaction takes usually place on a permissioned blockchain network that holds a set of data to be queried frequently. In this scope, the main problem of DLTs is their unsuitability for a fast service of complex queries on those data. In order to solve this issue, many proposals dump the ledger contents onto databases that, because of their own goals and design, are already optimized for the execution of those queries. Unfortunately, many of those proposals assume that the data to be queried consist in only a block or (cryptocurrency-related) transaction history. However, those organization consortiums commonly store other structured business-related information in the DLT, and there is an evident lack of support for querying that other kind of structured data. To remedy those problems, DELTA synchronizes, with minimal overhead, the DLT state into a database, providing: (1) a modular architecture with event-based handling of DLT updates that supports different DLTs and databases, (2) a transparent management, since DLT end users do not need to learn or use any new API in order to handle that synchronization (i.e., those users still rely on the original interface provided by their chosen DLT), (3) the efficient execution of complex queries on those structured data. Thus, DELTA reduces query times up to five orders of magnitude, depending on the DLT and the database, compared to queries directed to the ledger nodes.

Stablecoin is a medium of exchange with stable value in the world of decentralized finance (DeFi). In which, algorithmic stablecoins are one special type of stablecoins that are not backed by any asset. They stand to revolutionize the way a sovereign fiat operates. As implemented, algorithmic stablecoins are poorly stabilized in most cases; their prices easily deviate from the target or even fall into a catastrophic collapse, and are as a result often dismissed as a Ponzi scheme. However, what is the essence of Ponzi? In this paper, we try to clarify such a deceptive concept and reveal how algorithmic stablecoins work from a higher level. We find that Ponzi is basically a financial protocol that pays existing investors with funds collected from new ones. Running a Ponzi, however, does not necessarily imply that any participant is in any sense losing out, as long as the game can be perpetually rolled over. Economists call such realization as a rational Ponzi game. We thereby propose a rational model in the context of algorithmic stablecoins and draw its holding conditions. We apply the model to examine: whether or not the algorithmic stablecoin is a rational Ponzi game. Accordingly, we discuss two types of algorithmic stablecoins (Rebase & Seigniorage Shares) and dig into the historical market performance of a number of impactful projects to demonstrate the effectiveness of our model.

The information exchange methods currently used in the field of vehicle inspection are not in harmony with the social information construction environment, which hinders the interaction and flow of information. On the other hand, because a large amount of data collected by different types of sensors must be transmitted to edge computing nodes, dynamic vehicle detection will lead to network congestion and affect the calculation of edge computing nodes. Therefore, optimizing the data transmission between vehicles and edge computing nodes is a new challenge, which needs to be solved in the practical application of dynamic vehicle detection architecture based on edge computing. In this paper, we have considered the data requirements of dynamic vehicle detection in different environments, analyzed the optimization objectives and constraints, and proposed deviation detection and greedy algorithms to solve the problem of MILP solving the problem of long time and insufficient practical application, and evaluated the performance of the algorithm through simulation experiments using SUMO, a traffic flow simulation tool, and PreScan, a vehicle simulation test software. The results show that greedy algorithm is more suitable for practical application than deviation detection algorithm. The implementation of this system can improve the efficiency of vehicle detection and management, and has good application value.