Pricing GAN-based data generators under R?nyi differential privacy

As smart devices are becoming increasingly common in people's daily lives, privacy and security concerns make data collection expensive and limited, which further hinder the development of data-driven tasks. This paper studies how to better conduct private data trading via a novel generator method rather than direct trading of raw data. This new method facilitates more convenient data transactions by generator, protects the privacy of data owners and is satisfactory in terms of privacy compensation and query pricing. In detail, we propose RARIEA, a market framework for tRading privAte data geneRators based on GAN under renyI diffErential privAcy, which involves data owners, a data broker, and data consumers. To start, the broker employs the GAN training generator to augment the data to relieve the data shortage, introducing noise into its training process to preserve the owners' privacy. After that, the broker uses renyi differential privacy to quantify the privacy loss at the data item level during the GAN training process and compensates each owner according to their respective privacy policies. Finally, the data broker charges each of the data consumers for their queries, where the price is lower bounded by the total privacy compensation. We then evaluate the performance of RARIEA on classic data sets: MNIST, FashionMNIST, and CelebA. The analysis and simulation results reveal that the generator provided by RARIEA can not only meet the data consumers' demand for quantity and quality but also protect the owners' privacy. In addition, RARIEA not only allows finer control over data owner compensation, but also excels at controlling the data broker's revenue to improve market efficiency while ensuring fairness, balance, and monotonicity of pricing. (c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).