Computer Science Review最新文献

Set accumulators are cryptographic primitives used to represent arbitrarily large sets of elements with a single constant-size value and to efficiently verify whether a value belongs to that set. Accumulators support the generation of membership proofs, meaning that they can certify the presence of a given value among the elements of a set. In this paper we present an overview of the theoretical concepts underlying set accumulators, we compare the most popular constructions from a complexity perspective, and we survey a number of their applications related to blockchain technology. In particular, we focus on four different use cases: query authentication, stateless transactions validation, anonymity enhancement, and identity management. For each of these scenarios, we examine the main problems they introduce and discuss the most relevant accumulator-based solutions proposed in the literature. Finally, we point out the common approaches between the proposals and highlight the currently open problems in each field.

Wireless networks have been in focus since the last few decades due to their indispensable role in the future generation networks like the Internet of Things (IoT). However, the associated challenges in wireless network implementation such as distance, line-of-sight, interference, weather, power issues, etc., affect the performance adversely. Software Defined Networking (SDN) is a future generation networking technology and has been proven to alleviate the performance challenges in the existing wireless IoT networks. It helps to evolve the wireless IoT domain in the form of Software Defined Wireless Network based IoT (SDWN-IoT). Traffic Engineering (TE) has been part of traditional network designs since long back, to improve the performance of the communication networks. However, its more optimized forms and their usefulness in SDWN-IoT networks have been under active investigation. This work explores the existing literature related to the major types of SDWN-IoT networks namely, Software Defined Wireless Sensor Network based IoT (SDWSN-IoT) and Software Defined Wireless Mesh Network based IoT (SDWMN-IoT). Additionally, the article also draws some useful inferences, and compares respective contributions and shortcomings. Finally, various research opportunities and challenges have been discussed with respect to the SDWSN-IoT and SDWMN-IoT networks.

Open Science is a paradigm in which scientific data, procedures, tools and results are shared transparently and reused by society. The European Open Science Cloud (EOSC) initiative is an effort in Europe to provide an open, trusted, virtual and federated computing environment to execute scientific applications and store, share and reuse research data across borders and scientific disciplines. Additionally, scientific services are becoming increasingly data-intensive, not only in terms of computationally intensive tasks but also in terms of storage resources. To meet those resource demands, computing paradigms such as High-Performance Computing (HPC) and Cloud Computing are applied to e-science applications. However, adapting applications and services to these paradigms is a challenging task, commonly requiring a deep knowledge of the underlying technologies, which often constitutes a general barrier to its uptake by scientists. In this context, EOSC-Synergy, a collaborative project involving more than 20 institutions from eight European countries pooling their knowledge and experience to enhance EOSC’s capabilities and capacities, aims to bring EOSC closer to the scientific communities. This article provides a summary analysis of the adaptations made in the ten thematic services of EOSC-Synergy to embrace this paradigm. These services are grouped into four categories: Earth Observation, Environment, Biomedicine, and Astrophysics. The analysis will lead to the identification of commonalities, best practices and common requirements, regardless of the thematic area of the service. Experience gained from the thematic services can be transferred to new services for the adoption of the EOSC ecosystem framework. The article made several recommendations for the integration of thematic services in the EOSC ecosystem regarding Authentication and Authorization (federated regional or thematic solutions based on EduGAIN mainly), FAIR data and metadata preservation solutions (both at cataloguing and data preservation—such as EUDAT’s B2SHARE), cloud platform-agnostic resource management services (such as Infrastructure Manager) and workload management solutions.

In the modern digital world users need to make privacy and security choices that have far-reaching consequences. Researchers are increasingly studying people’s decisions when facing with privacy and security trade-offs, the pressing and time consuming disincentives that influence those decisions, and methods to mitigate them. This work aims to present a systematic review of the literature on privacy categorisation, which has been defined in terms of profile, profiling, segmentation, clustering and personae. Privacy categorisation involves the possibility to classify users according to specific prerequisites, such as their ability to manage privacy issues, or in terms of which type of and how many personal information they decide or do not decide to disclose. Privacy categorisation has been defined and used for different purposes. The systematic review focuses on three main research questions that investigate the study contexts, i.e. the motivations and research questions, that propose privacy categorisations; the methodologies and results of privacy categorisations; the evolution of privacy categorisations over time. Ultimately it tries to provide an answer whether privacy categorisation as a research attempt is still meaningful and may have a future.

In recent times, the machine learning (ML) community has recognized the deep learning (DL) computing model as the Gold Standard. DL has gradually become the most widely used computational approach in the field of machine learning, achieving remarkable results in various complex cognitive tasks that are comparable to, or even surpassing human performance. One of the key benefits of DL is its ability to learn from vast amounts of data. In recent years, the DL field has witnessed rapid expansion and has found successful applications in various conventional areas. Significantly, DL has outperformed established ML techniques in multiple domains, such as cloud computing, robotics, cybersecurity, and several others. Nowadays, cloud computing has become crucial owing to the constant growth of the IoT network. It remains the finest approach for putting sophisticated computational applications into use, stressing the huge data processing. Nevertheless, the cloud falls short because of the crucial limitations of cutting-edge IoT applications that produce enormous amounts of data and necessitate a quick reaction time with increased privacy. The latest trend is to adopt a decentralized distributed architecture and transfer processing and storage resources to the network edge. This eliminates the bottleneck of cloud computing as it places data processing and analytics closer to the consumer. Machine learning (ML) is being increasingly utilized at the network edge to strengthen computer programs, specifically by reducing latency and energy consumption while enhancing resource management and security. To achieve optimal outcomes in terms of efficiency, space, reliability, and safety with minimal power usage, intensive research is needed to develop and apply machine learning algorithms. This comprehensive examination of prevalent computing paradigms underscores recent advancements resulting from the integration of machine learning and emerging computing models, while also addressing the underlying open research issues along with potential future directions. Because it is thought to open up new opportunities for both interdisciplinary research and commercial applications, we present a thorough assessment of the most recent works involving the convergence of deep learning with various computing paradigms, including cloud, fog, edge, and IoT, in this contribution. We also draw attention to the main issues and possible future lines of research. We hope this survey will spur additional study and contributions in this exciting area.