3,497 research outputs found
Link Before You Share: Managing Privacy Policies through Blockchain
With the advent of numerous online content providers, utilities and
applications, each with their own specific version of privacy policies and its
associated overhead, it is becoming increasingly difficult for concerned users
to manage and track the confidential information that they share with the
providers. Users consent to providers to gather and share their Personally
Identifiable Information (PII). We have developed a novel framework to
automatically track details about how a users' PII data is stored, used and
shared by the provider. We have integrated our Data Privacy ontology with the
properties of blockchain, to develop an automated access control and audit
mechanism that enforces users' data privacy policies when sharing their data
across third parties. We have also validated this framework by implementing a
working system LinkShare. In this paper, we describe our framework on detail
along with the LinkShare system. Our approach can be adopted by Big Data users
to automatically apply their privacy policy on data operations and track the
flow of that data across various stakeholders.Comment: 10 pages, 6 figures, Published in: 4th International Workshop on
Privacy and Security of Big Data (PSBD 2017) in conjunction with 2017 IEEE
International Conference on Big Data (IEEE BigData 2017) December 14, 2017,
Boston, MA, US
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
BC4LLM: Trusted Artificial Intelligence When Blockchain Meets Large Language Models
In recent years, artificial intelligence (AI) and machine learning (ML) are
reshaping society's production methods and productivity, and also changing the
paradigm of scientific research. Among them, the AI language model represented
by ChatGPT has made great progress. Such large language models (LLMs) serve
people in the form of AI-generated content (AIGC) and are widely used in
consulting, healthcare, and education. However, it is difficult to guarantee
the authenticity and reliability of AIGC learning data. In addition, there are
also hidden dangers of privacy disclosure in distributed AI training. Moreover,
the content generated by LLMs is difficult to identify and trace, and it is
difficult to cross-platform mutual recognition. The above information security
issues in the coming era of AI powered by LLMs will be infinitely amplified and
affect everyone's life. Therefore, we consider empowering LLMs using blockchain
technology with superior security features to propose a vision for trusted AI.
This paper mainly introduces the motivation and technical route of blockchain
for LLM (BC4LLM), including reliable learning corpus, secure training process,
and identifiable generated content. Meanwhile, this paper also reviews the
potential applications and future challenges, especially in the frontier
communication networks field, including network resource allocation, dynamic
spectrum sharing, and semantic communication. Based on the above work combined
and the prospect of blockchain and LLMs, it is expected to help the early
realization of trusted AI and provide guidance for the academic community
Enabling Technologies for Web 3.0: A Comprehensive Survey
Web 3.0 represents the next stage of Internet evolution, aiming to empower
users with increased autonomy, efficiency, quality, security, and privacy. This
evolution can potentially democratize content access by utilizing the latest
developments in enabling technologies. In this paper, we conduct an in-depth
survey of enabling technologies in the context of Web 3.0, such as blockchain,
semantic web, 3D interactive web, Metaverse, Virtual reality/Augmented reality,
Internet of Things technology, and their roles in shaping Web 3.0. We commence
by providing a comprehensive background of Web 3.0, including its concept,
basic architecture, potential applications, and industry adoption.
Subsequently, we examine recent breakthroughs in IoT, 5G, and blockchain
technologies that are pivotal to Web 3.0 development. Following that, other
enabling technologies, including AI, semantic web, and 3D interactive web, are
discussed. Utilizing these technologies can effectively address the critical
challenges in realizing Web 3.0, such as ensuring decentralized identity,
platform interoperability, data transparency, reducing latency, and enhancing
the system's scalability. Finally, we highlight significant challenges
associated with Web 3.0 implementation, emphasizing potential solutions and
providing insights into future research directions in this field
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