One of the biggest concerns of big data is privacy. However, the study on big data privacy is
still at a very early stage. We believe the forthcoming solutions and theories of big data …

The problem of privacy-preserving data analysis has a long history spanning multiple
disciplines. As electronic data about individuals becomes increasingly detailed, and as …

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 …

When a user shares her data with online platforms, she reveals information about others. In
such a setting, externalities depress the price of data because once a user's information is …

The growing popularity and development of data mining technologies bring serious threat to
the security of individual,'s sensitive information. An emerging research topic in data mining …

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 …

In this work, we aim to design a data marketplace; a robust real-time matching mechanism to
efficiently buy and sell training data for Machine Learning tasks. While the monetization of …

Federated learning (FL) is a privacy-preserving distributed machine learning framework,
which involves training statistical models over a number of mobile users (ie, workers) while …

Differential privacy is becoming a gold standard notion of privacy; it offers a guaranteed
bound on loss of privacy due to release of query results, even under worst-case …

Marketplaces are often small parts of large markets, and both markets and marketplaces
come in many varieties. Market design seeks to understand what marketplaces must …