2,290 research outputs found
Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments
Decentralized systems are a subset of distributed systems where multiple
authorities control different components and no authority is fully trusted by
all. This implies that any component in a decentralized system is potentially
adversarial. We revise fifteen years of research on decentralization and
privacy, and provide an overview of key systems, as well as key insights for
designers of future systems. We show that decentralized designs can enhance
privacy, integrity, and availability but also require careful trade-offs in
terms of system complexity, properties provided, and degree of
decentralization. These trade-offs need to be understood and navigated by
designers. We argue that a combination of insights from cryptography,
distributed systems, and mechanism design, aligned with the development of
adequate incentives, are necessary to build scalable and successful
privacy-preserving decentralized systems
Metaverse Security and Privacy: An Overview
Metaverse is a living space and cyberspace that realizes the process of
virtualizing and digitizing the real world. It integrates a plethora of
existing technologies with the goal of being able to map the real world, even
beyond the real world. Metaverse has a bright future and is expected to have
many applications in various scenarios. The support of the Metaverse is based
on numerous related technologies becoming mature. Hence, there is no doubt that
the security risks of the development of the Metaverse may be more prominent
and more complex. We present some Metaverse-related technologies and some
potential security and privacy issues in the Metaverse. We present current
solutions for Metaverse security and privacy derived from these technologies.
In addition, we also raise some unresolved questions about the potential
Metaverse. To summarize, this survey provides an in-depth review of the
security and privacy issues raised by key technologies in Metaverse
applications. We hope that this survey will provide insightful research
directions and prospects for the Metaverse's development, particularly in terms
of security and privacy protection in the Metaverse.Comment: IEEE BigData 2022. 10 pages, 2 figure
Trustworthy Edge Machine Learning: A Survey
The convergence of Edge Computing (EC) and Machine Learning (ML), known as
Edge Machine Learning (EML), has become a highly regarded research area by
utilizing distributed network resources to perform joint training and inference
in a cooperative manner. However, EML faces various challenges due to resource
constraints, heterogeneous network environments, and diverse service
requirements of different applications, which together affect the
trustworthiness of EML in the eyes of its stakeholders. This survey provides a
comprehensive summary of definitions, attributes, frameworks, techniques, and
solutions for trustworthy EML. Specifically, we first emphasize the importance
of trustworthy EML within the context of Sixth-Generation (6G) networks. We
then discuss the necessity of trustworthiness from the perspective of
challenges encountered during deployment and real-world application scenarios.
Subsequently, we provide a preliminary definition of trustworthy EML and
explore its key attributes. Following this, we introduce fundamental frameworks
and enabling technologies for trustworthy EML systems, and provide an in-depth
literature review of the latest solutions to enhance trustworthiness of EML.
Finally, we discuss corresponding research challenges and open issues.Comment: 27 pages, 7 figures, 10 table
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