SoK: Confidential Quartet - Comparison of Platforms for Virtualization-Based Confidential Computing

Confidential computing allows processing sensitive workloads in securely isolated spaces. Following earlier adop- tion of process-based approaches to isolation, vendors are now enabling hardware and firmware support for virtualization-based confidential computing on several server platforms. Due to variations in the technology stack, threat model, implemen-tation and functionality, the available solutions offer somewhat different capabilities, trade-offs and security guarantees. In this paper we review, compare and contextualize four virtualization-based confidential computing technologies for enterprise server platforms - AMD SEV, ARM CCA, IBM PEF and Intel TDX

Performance Analysis of Blockchain-Enabled Security and Privacy Algorithms in Connected and Autonomous Vehicles: A Comprehensive Review

Strategic investment(s) in vehicle automation technologies led to the rapid development of technology that revolutionised transport services and reduced fatalities on a scale never seen before. Technological advancements and their integration in Connected Autonomous Vehicles (CAVs) increased uptake and adoption and pushed firmly for the development of highly supportive legal and regulatory and testing environments. However, systemic threats to the security and privacy of technologies and lack of data transparency have created a dynamic threat landscape within which the establishment and verification of security and privacy requirements proved to be an arduous task. In CAVs security and privacy issues can affect the resilience of these systems and hinder the safety of the passengers. Existing research efforts have been placed to investigate the security issues in CAVs and propose solutions across the whole spectrum of cyber resilience. This paper examines the state-of-the-art in security and privacy solutions for CAVs. It investigates their integration challenges, drawbacks and efficiencies when coupled with distributed technologies such as Blockchain. It has also listed different cyber-attacks being investigated while designing security and privacy mechanism for CAVs

Vertical Federated Learning:A Structured Literature Review

Federated Learning (FL) has emerged as a promising distributed learning paradigm with an added advantage of data privacy. With the growing interest in having collaboration among data owners, FL has gained significant attention of organizations. The idea of FL is to enable collaborating participants train machine learning (ML) models on decentralized data without breaching privacy. In simpler words, federated learning is the approach of ``bringing the model to the data, instead of bringing the data to the mode''. Federated learning, when applied to data which is partitioned vertically across participants, is able to build a complete ML model by combining local models trained only using the data with distinct features at the local sites. This architecture of FL is referred to as vertical federated learning (VFL), which differs from the conventional FL on horizontally partitioned data. As VFL is different from conventional FL, it comes with its own issues and challenges. In this paper, we present a structured literature review discussing the state-of-the-art approaches in VFL. Additionally, the literature review highlights the existing solutions to challenges in VFL and provides potential research directions in this domain