CSTIO Seminar with Dr Michelle Zhang (Durham University)

Integrated Private Data Trading Systems for Data Marketplaces

Abstract

In the digital age, data is a valuable commodity, and data marketplaces offer opportunities for data owners to monetise their private data. However, data privacy is a significant concern, and differential privacy has become a popular solution to address this issue. Private data trading systems (PDQSs) facilitate the trade of private data by determining which data owners to purchase data from, the amount of privacy purchased, and providing specific aggregation statistics while protecting the privacy of data owners. The current PDQS systems, which separate procurement and query processes, often suffer from over-perturbation of private data and lack trustworthiness. To resolve this issue, we propose a new PDQS framework that integrates procurement and query processes to minimize excessive data perturbation. Within this framework, we introduce two approaches: one based on a greedy algorithm and the other utilising a neural network. Our experimental results show that both of our mechanisms outperformed the traditional separated procurement and query mechanism in terms of accuracy, given the same budget constraints.

About the speaker

Michelle Zhang is a Postdoc Research Associate in the Department of Computer Science at Durham University. She holds a PhD degree in Information Systems from the University of Auckland (2022). Her research interests are algorithmic mechanism design and privacy computing.