Generative AI's Potential in the Creation of Synthetic Data

Supervised learning is dominant in machine learning. However, the biggest challenge of supervised methods is their dependency on labelled large datasets. Compiling high-quality datasets is expensive and difficult to scale, often failing machine learning solutions due to the need for labelled datasets. The issue is more pronounced for smaller organizations. Generative methods in deep learning can generate synthetic datasets that match labelled datasets, accelerating model training.

Companies that rely on data for decision-making usually need help with privacy, data accuracy, and insufficient data availability. Computers can generate synthetic data at cloud computing speed, with extreme programmability and automatic labelling. With synthetic data, machines can learn from other machines, not just humans.

Synthetic data supplies realistic data without privacy concerns. One can test machine learning models or develop software applications without compromising personal data.

A synthetic dataset has the same statistical properties as its real-world dataset. Still, it has different data points. A new dataset can be generated by creating a generative machine learning model based on a relational database and applying it to a second dataset.

When to use synthetic data?

Businesses must prioritize use cases before investing in privacy-enhancing technology. Synthetic data is sample data that mimics the original data without personal information. Although actual data may be more useful in certain situations, synthetic data can be as valuable.

Generating synthetic data can help build correct machine learning models, especially when highly imbalanced training data (e.g., one class has over 99% of instances).

How is synthetic data used for different purposes today?

Privacy-by-Design

Protecting privacy is one of the most significant applications of synthetic data. By using privacy-enhancing technologies, it is possible to supply mathematical guarantees that a machine-learning model will not memorize any individual user's data or secrets within the dataset.

Data Retention

Data retention is a crucial challenge for companies prioritizing regulatory compliance. When a company sets a data retention policy, it agrees to erase data after a certain period to follow government and industry regulations and to honour its commitment to its customers. Synthetic data supplies an exciting alternative to simply removing data. Companies may need to create new synthetic datasets as they approach the end of their data retention policies. Unlike the original real-world datasets, these new datasets do not rely on individuals or entities. Although customers may not be able to query raw data and customer events anymore, this is often not necessary or relevant after several years of data. However, customers can still query the synthetic data to create trend analyses, graphs, and trends by using the synthetic data as a replacement for the real-world data that has been removed.

Testing Software Products and Services

Due to privacy and security concerns, developers often struggle to use exact production data for development, testing, and staging environments. Testing environments often rely on manually de-identified production data snapshots or low-quality fake/mock data. Manual data anonymization for pre-production testing quickly becomes outdated and often misses sensitive data. Synthetic data person who creates realistic testing and feature development environments that mirror production data.

Training ML (Machine Learning) and AI Models

Synthetic data solves the need for more data, which is essential for machine learning models. These models are becoming increasingly important in making crucial decisions that affect our lives, such as deciding whether we are eligible for a loan or a job, diagnosing medical conditions, and communicating with various devices and our surroundings. Although machine learning models are powerful, they need help interpreting infrequent data. Developers and companies building machine learning algorithms usually need help getting more data, especially when the data needed is expensive or difficult to collect. For example, obtaining large volumes of medical test results, examples of cyber-attacks, or image and video data for training robotics can be prohibitively expensive or even impossible. Synthetic data provides a cost-effective and scalable alternative to working with correct customer data or manually generating and annotating added data.

Sharing Data Within Organizations

There is a trend in businesses towards smaller development teams with fewer developers but are given more product and feature ownership. Individual teams often create silos around data, resulting in limited access to databases or warehouses. It can take weeks or even months for other teams or business units within the company to access siloed data. Businesses often face difficulty finding sensitive data within their data lakes, lake houses, and data warehouses. Although centralizing data can help, a lack of knowledge about the exact location of sensitive data in each table or data lake often leads companies to restrict access to entire datasets, considering them as having customer data.

Consequently, only a tiny percentage of overall employees and business units are granted access to such data. Synthetic data can be used to create fully anonymized datasets in a data warehouse or data lake. Users across the business can easily query these datasets without going through lengthy approval processes. For users who require raw access to real-world data, access can be granted on a case-by-case basis. This cuts the roadblocks that hinder innovation and progress.

Sharing Data with Third Parties

Companies must share confidential data with external entities. Traditional anonymization and de-identification techniques need improvement to ensure privacy and security against modern data attacks, such as data linkage and joinability. As an illustration, let us take the example of the Netflix Prize Challenge. Netflix organized a competition that offered the team a $1 million prize that could outperform its algorithms in predicting movie preferences. The Netflix team manually anonymized 100 million movie reviews to prepare for the challenge. This process involved removing all personal information and leaving only the movie ID, user ID, date, and review rating fields intact. Creating a dataset of 100 million reviews using techniques like synthetic data is now possible. This can be achieved by training a model on the original dataset using differential privacy and then generating a synthetic dataset. This synthetic data set would have many real-world data insights and offer increased levels of privacy while only incurring a tiny loss to its overall accuracy - making it a practical choice for data analysis.

Creating Synthetic Marketplaces & Accessible Data Exchanges

Companies are exploring new revenue streams through marketplaces using synthetic data. Medical researchers, as well as healthcare and life sciences organizations, find this information particularly intriguing. This platform supplies secure access to medical providers and transferable data for over 65,000 patients nationwide, helping researchers and medical students.

Types of Synthetic Data

The purpose of synthetic data is to protect confidential information while keeping statistical features. Synthetic data is categorized into three types:

Fully Synthetic Data

This data is purely synthetic and has nothing from the original data. The data generator for this type typically finds the density

function of features in the actual data and estimates the parameters of these. After estimating the density functions, privacy-protected series are created randomly for each feature. If some features in the original data are replaced with synthetic data, the protected series of those features are mapped to the remaining features of the original data. This mapping ensures that the protected and real series are ranked similarly. Some classical techniques, such as bootstrap methods and multiple imputations, can generate fully synthetic data. This technique is decisive in privacy protection because the data is purely synthetic, and no actual data exists. This method also ensures that the data generated is truthful.

Partially Synthetic Data

This data replaces only values of selected sensitive features with synthetic values. Actual values are replaced if they pose a substantial risk of disclosure. This data generation technique preserves privacy. Multiple imputation and model-based methods generate partially synthetic data and impute missing values.

Hybrid Synthetic Data

The data presented here combines real and synthetic data. An actual record is randomly chosen and paired with a similar synthetic record, resulting in hybrid data to create a new record. This approach supplies the benefits of both fully and partially synthetic data, resulting in high utility and good privacy preservation. However, it requires more memory and processing time than the other two methods.

How do businesses generate synthetic data?

When generating synthetic data, businesses have several methods, including decision trees, deep learning techniques, and iterative proportional fitting. The method for generating synthetic data should be based on specific requirements and desired data utility for the intended purpose. After synthesizing data, the utility of synthetic data must be assessed by comparing it to actual data.

Synthetic data generation techniques

Neural networks can generate synthetic data with greater accuracy and complexity. They can handle much richer data distributions than traditional algorithms and synthesize unstructured data like images and video.

Three neural techniques are commonly employed to generate synthetic data.

Variational Auto-Encoder

An unsupervised algorithm learns data distribution and generates synthetic data via an encoded-decoded architecture. The model minimizes reconstruction error with iterative training.

Generative Adversarial Network

An algorithm uses two neural networks to create realistic fake data points. One network generates fake data, while the other distinguishes between real and fake samples. These models are called GANs, and although they can produce highly detailed and realistic synthetic data points, they are challenging to train and require a lot of computational power.

Diffusion Models

A neural network is trained to remove Gaussian noise from corrupted data, producing a clear image. These models are known as Diffusion models and have high training stability. They can produce high-quality results for both image and audio.

What are the challenges that come with generating synthetic data?

Synthetic data offers many advantages, but it also poses challenges:

Data quality

Ensuring quality in training data is essential, especially in the case of synthetic data. Synthetic data of high quality should capture the same statistical distribution and basic structure as the original data. However, synthetic data can differ from correct data in certain aspects that affect the model's performance. Hence, paying attention to the quality of both real and synthetic data is crucial.

Avoid homogenization

Ensuring diversity in model training is crucial for its success. Suppose the training data is homogeneous and only focuses on specific data points while ignoring others. In that case, the model will perform poorly when dealing with those other data types. As actual data is highly diverse, it is essential to generate synthetic data that can capture the full range of diversity. For instance, a training dataset for human faces should highlight various ages, genders, and ethnicities the algorithm is expected to handle.

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