Related papers: A Novel approach for Hybrid Database
Federated Learning (FL) represents a paradigm shift in the field of machine learning, offering an approach for a decentralized training of models across a multitude of devices while maintaining the privacy of local data. However, the…
Grid based systems require a database access mechanism that can provide seamless homogeneous access to the requested data through a virtual data access system, i.e. a system which can take care of tracking the data that is stored in…
Modern hardware is abundantly parallel and increasingly heterogeneous. The numerous processing cores have non-uniform access latencies to the main memory and to the processor caches, which causes variability in the communication costs.…
Database users have started moving toward the use of cloud computing as a service because it provides computation and storage needs at affordable prices. However, for most of the users, the concern of privacy plays a major role as they…
As cloud computing gains traction, data owners are outsourcing their data to cloud service providers (CSPs) for Database Service (DBaaS), bringing in a deviation of data ownership and usage, and intensifying privacy concerns, especially…
Elasticity is one important feature in modern cloud computing systems and can result in computation failure or significantly increase computing time. Such elasticity means that virtual machines over the cloud can be preempted under a short…
Auditability is crucial for data outsourcing, facilitating accountability and identifying data loss or corruption incidents in a timely manner, reducing in turn the risks from such losses. In recent years, in synch with the growing trend of…
In the field of database deduplication, the goal is to find approximately matching records within a database. Blocking is a typical stage in this process that involves cheaply finding candidate pairs of records that are potential matches…
Federated Learning (FL) is a distributed machine learning paradigm designed for privacy-sensitive applications that run on resource-constrained devices with non-Identically and Independently Distributed (IID) data. Traditional FL frameworks…
The Hyperledger Fabric is well known and the most prominent enterprise-grade permissioned blockchain. The architecture of the Hyperledger Fabric introduces a new architecture paradigm of simulate-order-validate and pluggable architecture,…
The proliferation of smart technologies and evolving privacy regulations such as the GDPR and CPRA has increased the need to manage fine-grained access control (FGAC) policies in database management systems (DBMSs). Existing approaches to…
Computational grids are believed to be the ultimate framework to meet the growing computational needs of the scientific community. Here, the processing power of geographically distributed resources working under different ownerships, having…
Large organizations are seeking to create new architectures and scalable platforms to effectively handle data management challenges due to the explosive nature of data rarely seen in the past. These data management challenges are largely…
The growth of local data annually implies extra charges for the customers, which makes their business slowing down. Cloud computing paradigm comes with new technologies that offer a very economic and cost-effective solution, but the…
A traditional database systems is organized around a single data model that determines how data can be organized, stored and manipulated. But the vision of this paper is to develop new principles and techniques to manage multiple data…
In edge computing environments, app vendors can cache their data to be shared with their users in many geographically distributed edge servers. However, the cached data is particularly vulnerable to several intentional attacks or…
Modern Internet of Things (IoT) applications generate enormous amounts of data, making data-driven machine learning essential for developing precise and reliable statistical models. However, data is often stored in silos, and strict…
Order-preserving encryption (OPE) is a fundamental cryptographic tool for enabling efficient range queries on encrypted data in outsourced databases. Despite its importance, existing OPE schemes face critical limitations that hinder their…
Edge computing decentralizes computing resources, allowing for novel applications in domains such as the Internet of Things (IoT) in healthcare and agriculture by reducing latency and improving performance. This decentralization is achieved…
Researchers have long touted a vision of the future enabled by a proliferation of internet-of-things devices, including smart sensors, homes, and cities. Increasingly, embedding intelligence in such devices involves the use of deep neural…