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Blockchain is an emerging decentralized data collection, sharing and storage technology, which have provided abundant transparent, secure, tamper-proof, secure and robust ledger services for various real-world use cases. Recent years have…

Decentralized and federated learning algorithms face data heterogeneity as one of the biggest challenges, especially when users want to learn a specific task. Even when personalized headers are used concatenated to a shared network…

This paper presents MindTheDApp, a toolchain designed specifically for the structural analysis of Ethereum-based Decentralized Applications (DApps), with a distinct focus on a complex network-driven approach. Unlike existing tools, our…

Data marketplaces (DMs) promote the benefits of the Internet of Things (IoT) in smart cities. To facilitate the easy exchanges of real-time IoT data streams between device owners and third-party applications, it is required to provide…

Blockchain technology streamlines multi-party collaborations in decentralized settings, especially when trust is limited or difficult to establish. While public blockchains enhance transparency and reliability by replicating data across all…

A wide range of services and applications can be improved and/or solved by using distributed ledger technology (DLT). These services and applications have widely varying quality of service (QoS) requirements. However, most existing DLT…

In recent years, many decentralized applications based on blockchain (DApp) have been developed. However, due to inadequate testing, DApps are easily exposed to serious vulnerabilities. We find three main challenges for DApp testing, i.e.,…

Blockchains and distributed ledger technology offer promising capabilities for supporting collaborative business processes across organizations. Typically, approaches in this field fall into two categories: either executing the entire…

This work presents the design and implementation of a decentralized application (DApp) that aims to guarantee the privacy of data related to the health area, which are stored and shared within a blockchain network. For this, encryption with…

Most of today's online services (e.g., social networks, search engines, market places) are centralized, which is recognized as unsatisfactory by a majority of users for various reasons (e.g., centralized governance, censorship, loss of…

In recent years, decentralized applications (dApps) built on blockchain platforms such as Ethereum and coded in languages such as Solidity, have gained attention for their potential to disrupt traditional centralized systems. Despite their…

Blockchain uses the idea of storing transaction data in the form of a distributed ledger wherein each node in the network stores a current copy of the sequence of transactions in the form of a hash chain. This requirement of storing the…

Federated Identity Management has proven its worth by offering economic benefits and convenience to Service Providers and users alike. In such federations, the Identity Provider (IdP) is the solitary entity responsible for managing user…

This paper introduces OPTIMUM-DERAM, a highly consistent, scalable, secure, and decentralized shared memory solution. Traditional distributed shared memory implementations offer multi-object support by multi-threading a single object memory…

As distributed learning applications such as Federated Learning, the Internet of Things (IoT), and Edge Computing grow, it is critical to address the shortcomings of such technologies from a theoretical perspective. As an abstraction, we…

The disaggregated memory (DM) architecture offers high resource elasticity at the cost of data access performance. While caching frequently accessed data in compute nodes (CNs) reduces access overhead, it requires costly centralized…

Internet technologies have made a paradigm shift in the fields of computing and data science and one such paradigm defining change is the Internet of Things or IoT. Nowadays, thousands of household appliances use integrated smart devices…

Federated Learning (FL) is a machine learning method for training with private data locally stored in distributed machines without gathering them into one place for central learning. Despite its promises, FL is prone to critical security…

Federated learning (FL) has received a surge of interest in recent years thanks to its benefits in data privacy protection, efficient communication, and parallel data processing. Also, with appropriate algorithmic designs, one could achieve…

Allocation of resources and their control over multiple organisations is challenging. This is especially true for a large-scale and dynamic system like the Internet of Things (IoT). One of the core issues in such a system is the provision…