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Synthetic Data – key benefits, types, generation methods, and challenges!

. . . Here's a beginner guide on what you should know about synthetic data.

Image Source: Unsplash
Image Source: Unsplash

Researchers and data scientists often come across situations where they either do not have the real data or can not make use of it due to confidentiality or privacy concerns. To overcome this problem, synthetic data generation is carried out to create a replacement of real data. For the right functioning of the algorithm, the right replacement of real data needs to be done which further should be realistic in nature. The study presented in this article is with respect to the growing demand for synthetic data in Artificial Intelligence and how we can generate this data.

Introduction

Synthetic Data is data that is created manually or artificially apart from the data generated by real-world events. Various algorithms and tools are there to help us generate synthetic data which is used in a wide variety of ways. This is generally needed to validate the model and to compare behavioral aspects of real data with the ones generated by the model. The origination of synthetic data dates back to the ’90s, but the true usage came in the past few years with people getting to know the risks in data science that can fairly be eliminated with the usage of synthetic data.

Importance of Synthetic Data

The importance of synthetic data comes with its power of generating features to meet specific needs or conditions which otherwise would not be available in real-world data. When there is a lack of data for testing or when privacy is your utmost priority, synthetic data comes to the rescue.

AI business world has an abundance of dependency on synthetic data –

  • In the medical and healthcare sector, synthetic data is used for testing certain conditions and cases for which real data does not exist.
  • ML-based Uber and Google’s self-driving cars are trained with the use of synthetic data.
  • In the financial sector, fraud detection and protection are very critical. New fraudulent cases can be examined with the help of synthetic data.
  • Synthetic data enables data professionals to access the use of centrally recorded data while still maintaining the confidentiality of the data. Synthetic data comes with the power to replicate the important features of real data without exposing the true sense of it, thereby keeping privacy intact.
  • In the research department, synthetic data helps you develop and deliver innovative products for which necessary data otherwise might not be available.

Methodologies

Majorly there are two ways to generate synthetic data –

  1. Drawing numbers from a distribution: The key idea is to observe the statistical distribution of real-world data and then replicate the same to produce similar data with simple numbers.
  2. Agent-based modeling: The key idea is to create a physical model of the observed statistical distribution of real-world data, then reproduce random data using the same model. It focuses on understanding the impact of the interaction between agents that directly affects the system as a whole.

Machine Learning with Synthetic Data

Machine Learning algorithms require a good amount of data to be processed in order to create a robust and reliable model. Generating such a huge amount of data would be difficult otherwise but with synthetic data, it becomes far easier. It can be of great importance to fields like Computer Vision or Image Processing where once an initial synthetic data is developed, model creation becomes easier.

Generative Adversarial Networks (GANs) were introduced recently and are a breakthrough in the field of image recognition. Generally composed of two networks: one discriminator and one generator. The functioning of the generator network is to generate synthetic images much closer to real-world images while the discriminator network targets to identify the real images out of synthetic ones. GANs are a part of the neural network family in machine learning, where both the networks keep learning and improving by building new nodes and layers.

Generating Synthetic Data comes with the flexibility to adjust its nature and environment as and when required in order to improve the performance of the model. Accuracy for labeled real-time data is sometimes quite expensive while accuracy for synthetic data can be easily achieved with a good score.

Types of Synthetic Data

The synthetic data is randomly generated with the intent to hide sensitive private information and retain statistical information of features in original data. Synthetic data is broadly classified into three categories:

  • Fully Synthetic Data – This data is purely synthetic and does not have anything from original data. The data generator for this type will typically identify the density function of features in the real data and estimate the parameters of these. Later for each feature, privacy-protected series are generated on a random basis from the estimated density functions. If only a few features of real data are selected for replacement with synthetic data, then the protected series of these features are mapped to the other features of the real data in order to rank the protected series and the real series in the same order. Few classical techniques used to generate fully synthetic data can be bootstrap methods and multiple imputations. Since the data is purely synthetic and that no real data exists, this technique has strong privacy protection with a fallback on the truthfulness of the data.
  • Partially Synthetic Data – This data replaces only values of some selected sensitive feature with the synthetic values. The real values, in this case, are replaced only if it contains a high risk of disclosure. This is done to preserve privacy in the newly generated data. Techniques used to generate partially synthetic data are multiple imputation and model-based techniques. These techniques are also helpful for imputing missing values in real data.
  • Hybrid Synthetic Data – This data is generated using both real and synthetic data. For each random record of real data, a close record in the synthetic data is chosen and then both are combined to form hybrid data. It provides advantages of both fully and partially synthetic data. It, therefore, is known to provide good privacy preservation with high utility compared to the other two but at a fallback of more memory and processing time.

Challenges

Synthetic data has strong roots in Artificial Intelligence with numerous benefits but still has some challenges which need to be taken care of while dealing with synthetic data. These are as follows:

  • Difficulty in generating synthetic data.
  • A number of inconsistencies encountered while replicating the complexities from real data to synthetic data.
  • The flexible nature of synthetic data makes it biased in behavior.
  • Validating it with synthetic test data might not be enough for users. They might require you to validate it with real data.
  • There could be some hidden follies on the performance of algorithms trained with simplified representations of synthetic data which lately may pop out while dealing with real data.
  • Many users may not accept synthetic data to be valid.
  • Replicating all necessary features from real data might become complex in nature. There also can be a possibility of missing out on some necessary features during this procedure.

Case Studies

Synthetic data has a lot of operational use cases. Some of the famous use cases are as follows –

Thanks for reading! 🙂

About Author

Kajal Singh is a Data Scientist and a Tutor at the Artificial Intelligence – Cloud and Edge implementations course at the University of Oxford. She is also the co-author of the book "Applications of Reinforcement Learning to Real-World Data (2021)"

References

https://www.riaktr.com/synthetic-data-become-major-competitive-advantage/

https://www.techworld.com/data/what-is-synthetic-data-how-can-it-help-protect-privacy-3703127/

https://blog.aimultiple.com/synthetic-data/

https://mro.massey.ac.nz/bitstream/handle/10179/11569/02_whole.pdf?sequence=2&isAllowed=y

https://tdwi.org/articles/2019/06/28/adv-all-synthetic-data-ultimate-ai-disruptor.aspx

https://www.techrepublic.com/resource-library/whitepapers/re-identification-and-synthetic-data-generators-a-case-study/

https://arxiv.org/pdf/1909.11512.pdf


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