Revealing the landscape of privacy-enhancing technologies in the context of data markets for the IoT: A systematic literature review
IoT data markets in public and private institutions have become increasingly relevant in
recent years because of their potential to improve data availability and unlock new business …
recent years because of their potential to improve data availability and unlock new business …
On the convergence of artificial intelligence and distributed ledger technology: A sco** review and future research agenda
Developments in artificial intelligence (AI) and distributed ledger technology (DLT) currently
lead to lively debates in academia and practice. AI processes data to perform tasks that were …
lead to lively debates in academia and practice. AI processes data to perform tasks that were …
Federated learning on non-iid data silos: An experimental study
Due to the increasing privacy concerns and data regulations, training data have been
increasingly fragmented, forming distributed databases of multiple “data silos”(eg, within …
increasingly fragmented, forming distributed databases of multiple “data silos”(eg, within …
Efficient task-specific data valuation for nearest neighbor algorithms
Given a data set $\mathcal {D} $ containing millions of data points and a data consumer who
is willing to pay for\$$ X $ to train a machine learning (ML) model over $\mathcal {D} $, how …
is willing to pay for\$$ X $ to train a machine learning (ML) model over $\mathcal {D} $, how …
A survey on data pricing: from economics to data science
J Pei - IEEE Transactions on knowledge and Data …, 2020 - ieeexplore.ieee.org
Data are invaluable. How can we assess the value of data objectively, systematically and
quantitatively? Pricing data, or information goods in general, has been studied and practiced …
quantitatively? Pricing data, or information goods in general, has been studied and practiced …
Efficient privacy-preserving machine learning for blockchain network
A blockchain as a trustworthy and secure decentralized and distributed network has been
emerged for many applications such as in banking, finance, insurance, healthcare and …
emerged for many applications such as in banking, finance, insurance, healthcare and …
Rethinking data heterogeneity in federated learning: Introducing a new notion and standard benchmarks
Though successful, federated learning (FL) presents new challenges for machine learning,
especially when the issue of data heterogeneity, also known as Non-IID data, arises. To …
especially when the issue of data heterogeneity, also known as Non-IID data, arises. To …
Leveraging public-private blockchain interoperability for closed consortium interfacing
With the increasing adoption of private blockchain platforms, consortia operating in various
sectors such as trade, finance, logistics, etc., are becoming common. Despite having the …
sectors such as trade, finance, logistics, etc., are becoming common. Despite having the …
Enabling execution assurance of federated learning at untrusted participants
Federated learning (FL), as a privacy-preserving machine learning framework, draws
growing attention in both industry and academia. It obtains a jointly accurate model by …
growing attention in both industry and academia. It obtains a jointly accurate model by …
Data pricing in machine learning pipelines
Abstract Machine learning is disruptive. At the same time, machine learning can only
succeed by collaboration among many parties in multiple steps naturally as pipelines in an …
succeed by collaboration among many parties in multiple steps naturally as pipelines in an …