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 …

Data privacy arises as one of the most important concerns, facing the pervasive
commoditization of big data statistic analysis in Internet of Things (IoT). Current solutions are …

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 …

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 …

The data collected in Internet of Thing (IoT) systems (IoT data) have stimulated dramatic
extension to the boundary of commercialized data statistic analysis, owing to the pervasive …

Data analytics using machine learning (ML) has become ubiquitous in science, business
intelligence, journalism and many other domains. While a lot of work focuses on reducing …

Data-driven machine learning has become ubiquitous. A marketplace for machine learning
models connects data owners and model buyers, and can dramatically facilitate data-driven …

A data marketplace is an online venue that brings data owners, data brokers, and data
consumers together and facilitates commoditisation of data amongst them. Data pricing, as a …

The vast advances in Machine Learning over the last ten years have been powered by the
availability of suitably prepared data for training purposes. The future of ML-enabled …

With the commoditization of personal privacy, pricing private data has become an intriguing
problem. In this paper, we study noisy aggregate statistics trading from the perspective of a …