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Cloud Computing holds the potential to eliminate the requirements for setting up of high-cost computing infrastructure for IT-based solutions and services that the industry uses. It promises to provide a flexible IT architecture, accessible…
Internet-of-things (IoT) is perpetually revolutionizing our daily life and rapidly transforming physical objects into an ubiquitous connected ecosystem. Due to their massive deployment and moderate security levels, those devices face a lot…
Distributed machine learning systems require strong privacy guarantees, verifiable compliance, and scalable deployment across heterogeneous and multi-cloud environments. This work introduces a cloud-native privacy-preserving architecture…
The mass integration and deployment of intelligent technologies within critical commercial, industrial and public environments have a significant impact on business operations and society as a whole. Though integration of these critical…
The cloud computing paradigm is being adopted by many organizations in different application domains as it is cost effective and offers a virtually unlimited pool of resources. Engineering critical systems can benefit from clouds in…
The rapid deployment of deep neural network (DNN) accelerators in safety-critical domains such as autonomous vehicles, healthcare systems, and financial infrastructure necessitates robust mechanisms to safeguard data confidentiality and…
The Cloud Computing concept offers dynamically scalable resources provisioned as a service over the Internet.Economic benefits are the main driver for the Cloud, since it promises the reduction of capital expenditure and operational…
Most cloud platforms have a Function-as-a-Service (FaaS) offering that enables users to easily write highly scalable applications. To better understand how the platform's architecture impacts its performance, we present a research-focused…
The object-capability model is a security measure that consists in encoding access rights in individual objects to restrict its interactions with other objects. Since its introduction in 2013, different approaches to object-capability have…
The cloud computing platform gives people the opportunity for sharing resources, services and information among the people of the whole world. In private cloud system, information is shared among the persons who are in that cloud. For this,…
We propose a capability-based access control technique for sharing Web resources, based on Verifiable Credentials (VCs) and OAuth 2.0. VCs are a secure means for expressing claims about a subject. Although VCs are ideal for encoding…
Security architectures providing Trusted Execution Environments (TEEs) have been an appealing research subject for a wide range of computer systems, from low-end embedded devices to powerful cloud servers. The goal of these architectures is…
The modern pervasiveness of large-scale deep neural networks (NNs) is driven by their extraordinary performance on complex problems but is also plagued by their sudden, unexpected, and often catastrophic failures, particularly on…
Recent increases in the computational demands of deep neural networks (DNNs) have sparked interest in efficient deep learning mechanisms, e.g., quantization or pruning. These mechanisms enable the construction of a small, efficient version…
The growing scale of data requires efficient memory subsystems with large memory capacity and high memory performance. Disaggregated architecture has become a promising solution for today's cloud and edge computing for its scalability and…
It has been argued that Event Calculus (EC) is suitable for modeling high-level specifications of safety-critical cyber-physical systems. The primary advantage lies in the rather small semantic gap between EC models and requirements…
The rapid growth of data-intensive applications such as generative AI, scientific simulations, and large-scale analytics is driving modern supercomputers and data centers toward increasingly heterogeneous and tightly integrated…
The continuing advancement of memory technology has not only fueled a surge in performance, but also substantially exacerbate reliability challenges. Traditional solutions have primarily focused on improving the efficiency of protection…
Despite the functional success of deep neural networks (DNNs), their trustworthiness remains a crucial open challenge. To address this challenge, both testing and verification techniques have been proposed. But these existing techniques…
The ever-increasing computation complexity of fast-growing Deep Neural Networks (DNNs) has requested new computing paradigms to overcome the memory wall in conventional Von Neumann computing architectures. The emerging Computing-In-Memory…