# Fake It Till You Make It: Guidelines for Effective Synthetic Data Generation
## 1 Introduction  
### Background  
Big data is profoundly changing the way organizations perform their businesses. Examples of big data applications include enhanced business intelligence, personalized marketing and cost reduction planning. The possibilities are endless as the availability of massive data is inspiring people to explore many different and life changing applications. One of the most promising fields where big data can be applied to make a change is healthcare [1]. The application of machine learning to health data has the potential to predict and better tackle epidemics, improve quality of care, reduce healthcare costs and advance personalized treatments [2; 3; 4]. Yet, these improvements are not yet fully realized. The main reason is the limited access to health data due to valid privacy concerns. In fact, releasing and sharing health data entails the risk of compromising the confidentiality of sensitive information and the privacy of the involved individuals [5]. It is governed by ethical and legal standards such as the EU's General Directive on Data Protection GDPR and the Health Insurance Portability and Accountability Act privacy rule of the U.S. (HIPAA privacy rule) [6; 7]. Under these regulations, it is very hard to share high quality data with analysts without informed consent from the data subjects [8]. In fact, many projects are discarded before they begin due to delays to acquire data [9]. Typically, 18 months is the average timeframe to establish a data sharing agreement [10].  
Anonymization is another approach for addressing privacy legislations [11; 12; 13], it is defined as a \"technique to prevent identification taking into account all the means reasonably likely to be used to identify a natural person\" [6; 14]. It is a process of information sanitization that produces data that cannot be linked back to its originators. However, the effectiveness of anonymization has been called into question as the possibility of re-identification through demographic, clinical, and genomic data has been demonstrated in various circumstances [15; 16].  
An increasingly popular way to overcome the issues of data availability is to use fully synthetic data. Fully Synthetic data generates an artificial dataset that contains no identifiable information about the dataset it was generated from and is considered a safe approach for the sharing of sensitive data [17; 18]. It was proposed by [PERSON] in 1993 to solve the issues of privacy and availability [19]. It has been argued that fully synthetic data does not carry privacy risks as it does not map to real individuals [18; 20; 21]. The application of Synthetic data to the healthcare domain is recent yet increasing in popularity. The recent 2020 future of privacy forum, cited synthetic data generation as a crucial privacy enhancing technology for the coming decade [22].  
Research in the synthetic data domain is thriving in two main directions: (i) some investigators are designing new synthetic data generation mechanisms [23; 24; 25; 26], while (ii) others are exploring the efficiency of such generators in real-life scenarios [27; 28; 29; 30; 31; 32; 33]. In this paper, we take a new and essential direction into investigating the effect of various data generation and usage settings on the quality of the generated data and its ensuing models. The goal is to inform on the best strategies to follow when generating and using synthetic data.