The field of Data Science and Machine Learning is growing every single day. As new models and algorithms are being proposed with time, these new algorithms and models need enormous data for training and testing. Deep Learning models are gaining so much popularity nowadays, and those models are also data-hungry. Obtaining such a massive amount of data in the context of the different problem statements is quite a hideous, time-consuming, and expensive process. The data is gathered from real-life scenarios, which raises security liabilities and privacy concerns. Most of the data is private and protected by privacy laws and regulations, which hinders the sharing and movement of data between organizations or sometimes between different departments of a single organization—resulting in delaying experiments and testing of products. So the question arises how can this issue be solved? How can the data be made more accessible and open without raising concerns about someone’s privacy?
The solution to this problem is something known as Synthetic data.