Generating Artificial Data for Private Deep Learning

2018/03/08 by Aleksei Triastcyn, Triastcyn, Aleksei, Boi Faltings +1
Computer Science · #Cryptography and Data Security #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques

paper · pdf · doi:10.48550/arxiv.1803.03148

openalex publication_date 2018/03/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper, we propose generating artificial data that retain statistical properties of real data as the means of providing privacy with respect to the original dataset. We use generative adversarial network to draw privacy-preserving artificial data samples and derive an empirical method to assess the risk of information disclosure in a differential-privacy-like way. Our experiments show that we are able to generate artificial data of high quality and successfully train and validate machine learning models on this data while limiting potential privacy loss.

Citations

• Gradient-based learning applied to document recognition
• Multidimensional binary search trees used for associative searching
• Scalable Private Learning with PATE
• Differentially Private Generative Adversarial Network

Related

• Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees
• A Unified View of Differentially Private Deep Generative Modeling
• Differentially Private Synthetic Data: Applied Evaluations and Enhancements
• imdpGAN: Generating Private and Specific Data with Generative Adversarial Networks
• Security and Privacy Preserving Deep Learning