The emergence of machine learning models is primarily aimed at helping humans solve many problems in their lives and even discovering new insights from data. Training models require a significant amount of data to be inputted. However, collecting real-life data on behavior is becoming increasingly challenging as privacy concerns increase. Therefore, the assistance of synthetic data is necessary to obtain more data for training and improve the accuracy of the models. Experts estimate that by 2024, up to 60% of the training data will be synthetic, and by 2030, most will be synthetic. This highlights the importance of synthetic data in the future.
What are the methods for generating synthetic data?
Synthetic data is generated based on the features and structure of the original data in order to simulate a similar data distribution. However, the resulting data did not deviate significantly from the original data. Models trained on artificially synthesized data also exhibit similar accuracy to models trained on the original data.
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Why is synthetic data important?
Addressing data scarcity or insufficiency: In situations where training models are limited to emerging technology domains or rare disease research, actual data may be challenging to collect or insufficient in quantity. Synthetic data can be used to simulate and generate data that closely resembles the real world, filling in the gaps and providing a more comprehensive dataset.
Resolving data privacy and security issues: Real data often contains sensitive information such as personal identities or financial data. To protect privacy and data security, the use of synthetic data can eliminate privacy concerns and provide a large volume of high-quality and useful data.
Removing biases from data: Real-world data can sometimes contain biased or skewed content, which can lead to models learning incorrect information and subsequently making inaccurate judgments or predictions. Using synthetic data can help reduce data biases and improve the fairness and impartiality of models.
What are the advantages of synthetic data?
Customization of desired data: Collecting high-quality real-time data can be difficult, expensive, and time-consuming. However, synthetic data techniques allow users to quickly and easily generate the desired data, customized according to specific needs.
Full control over data variables for improved accuracy: Synthetic data provides complete control over all data variables, including the level of data separation, sample size, and noise within the dataset. This control allows for fine-tuning and optimization to enhance the accuracy of the models.
Reduction in data collection time: Since synthetic data does not require collecting data from real-world events, it can be rapidly created using appropriate techniques, saving time in the data collection process.
Mitigation of privacy concerns: Synthetic data is not derived from real data, which eliminates concerns related to user privacy and the potential disclosure of sensitive information, especially when dealing with sensitive health data.
Application of synthetic data
Financial services data: Financial institutions may need to build models for assessing customer creditworthiness using machine learning. However, using real customer data for model training can raise privacy concerns. Synthetic data techniques can be applied to simulate real data, providing a solution when actual data is unavailable while ensuring privacy protections.
Healthcare data: When developing models to analyze the probability of a rare disease, a large amount of data from patients who have had the disease in the past may be required. However, accessing and using the health care data of these patients may involve compliance with medical regulations, and the number of individuals with these rare mutations may be limited. Synthetic data can overcome data scarcity issues by providing large quantities of privacy-preserving data for training models in healthcare applications.
Summary
Synthetic data is expected to be a major trend in data applications in the future. It eliminates privacy concerns associated with real data and allows for the acquisition of large volumes of data for model training quickly and at a low cost. While synthetic data offers many advantages, there are also challenges and limitations to consider. One challenge is ensuring that synthetic data accurately replicates the features and structure of complex real-world data. Additionally, accurately assessing the quality and effectiveness of synthetic data is another important task to address. These are the key issues that need to be addressed as synthetic data becomes more prevalent in data applications.
