5,477 research outputs found
Social Metaverse: Challenges and Solutions
Social metaverse is a shared digital space combining a series of
interconnected virtual worlds for users to play, shop, work, and socialize. In
parallel with the advances of artificial intelligence (AI) and growing
awareness of data privacy concerns, federated learning (FL) is promoted as a
paradigm shift towards privacy-preserving AI-empowered social metaverse.
However, challenges including privacy-utility tradeoff, learning reliability,
and AI model thefts hinder the deployment of FL in real metaverse applications.
In this paper, we exploit the pervasive social ties among users/avatars to
advance a social-aware hierarchical FL framework, i.e., SocialFL for a better
privacy-utility tradeoff in the social metaverse. Then, an aggregator-free
robust FL mechanism based on blockchain is devised with a new block structure
and an improved consensus protocol featured with on/off-chain collaboration.
Furthermore, based on smart contracts and digital watermarks, an automatic
federated AI (FedAI) model ownership provenance mechanism is designed to
prevent AI model thefts and collusive avatars in social metaverse. Experimental
findings validate the feasibility and effectiveness of proposed framework.
Finally, we envision promising future research directions in this emerging
area.Comment: Accepted by Internet of Things Magazine in 23-May 202
Viewpoint | Personal Data and the Internet of Things: It is time to care about digital provenance
The Internet of Things promises a connected environment reacting to and
addressing our every need, but based on the assumption that all of our
movements and words can be recorded and analysed to achieve this end.
Ubiquitous surveillance is also a precondition for most dystopian societies,
both real and fictional. How our personal data is processed and consumed in an
ever more connected world must imperatively be made transparent, and more
effective technical solutions than those currently on offer, to manage personal
data must urgently be investigated.Comment: 3 pages, 0 figures, preprint for Communication of the AC
Trust and obfuscation principles for quality of information in emerging pervasive environments
Non peer reviewedPostprin
The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges
The Internet of Things (IoT) refers to a network of connected devices
collecting and exchanging data over the Internet. These things can be
artificial or natural, and interact as autonomous agents forming a complex
system. In turn, Business Process Management (BPM) was established to analyze,
discover, design, implement, execute, monitor and evolve collaborative business
processes within and across organizations. While the IoT and BPM have been
regarded as separate topics in research and practice, we strongly believe that
the management of IoT applications will strongly benefit from BPM concepts,
methods and technologies on the one hand; on the other one, the IoT poses
challenges that will require enhancements and extensions of the current
state-of-the-art in the BPM field. In this paper, we question to what extent
these two paradigms can be combined and we discuss the emerging challenges
Location proof systems for smart internet of things:Requirements, taxonomy, and comparative analysis
Security and Privacy Issues of Big Data
This chapter revises the most important aspects in how computing
infrastructures should be configured and intelligently managed to fulfill the
most notably security aspects required by Big Data applications. One of them is
privacy. It is a pertinent aspect to be addressed because users share more and
more personal data and content through their devices and computers to social
networks and public clouds. So, a secure framework to social networks is a very
hot topic research. This last topic is addressed in one of the two sections of
the current chapter with case studies. In addition, the traditional mechanisms
to support security such as firewalls and demilitarized zones are not suitable
to be applied in computing systems to support Big Data. SDN is an emergent
management solution that could become a convenient mechanism to implement
security in Big Data systems, as we show through a second case study at the end
of the chapter. This also discusses current relevant work and identifies open
issues.Comment: In book Handbook of Research on Trends and Future Directions in Big
Data and Web Intelligence, IGI Global, 201
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