Generative Adversarial Nets Enhanced Continual Data Release Using Differential Privacy

Abstract

In the era of big data, increasing massive volume of data is generated and published consecutively for both research and commercial purposes. The potential value of sensitive information also attracts interest from adversaries and thereby arises public concern. Current research mostly focuses on privacy-preserving data release in a statistic manner rather than taking the dynamics and correlation of context into consideration. Motivated by this, a novel idea is proposed by combining differential privacy and generative adversarial nets. Generative adversarial nets and its extensions are used to generate a synthetic data set with indistinguishable statistic features while differential privacy guarantees a trade-off between the privacy protection and data utility. Extensive simulation results on real-world data set testify the superiority of the proposed model in terms of privacy protection and improved data utility.