Deep Learning: Differential Privacy Preservation in the Era of Big Data
Jalpesh Vasa, Amit Thakkar
Abstract
In recent years, deep learning (DL) has been ubiquitous in several areas, such as text recognition and data analysis, limited by this and increasingly used in security and data protection applications. Thus, the DL method has achieved remarkable big data analysis growth to avoid different attacks. This paper presents different methods for protecting privacy for DL in big data analysis. First, some possible attacks are explained, and then some basic approaches to protecting privacy in big data platforms are explained. In each section, drawbacks of the corresponding attacks are elaborated, and DL-based methods’ effectiveness in privacy preservation has been discussed. Finally, an effective solution for enhancing privacy preservation in DL models is given. The several DL-based privacy preservation methods for big data analysis and their advantages and disadvantages are elaborated. At last, drawbacks of DL based methods are highlighted, and future scope is given to address these issues.