To synthesize data is to create fake data that mimics the characteristics and patterns of real information to protect the identities of the data subjects.
Table of Contents
What Does it Mean to Synthesize Data?
Methods of Data Synthesis
Differential Privacy: Enhancing Privacy in Synthetic Data
Data Anonymization vs Synthetic Data Generation
Synthetic Data Generation Techniques
Benefits of Synthesizing Data
Synthesize Data with a Business Entity Approach
What Does it Mean to Synthesize Data?
What is synthetic data, and why synthesize data in the first place?
Data synthesis refers to the process of generating artificial data that closely mimics the statistical properties and structure of real data. It involves creating a realistic dataset without directly using sensitive or Personally Identifiable Information (PII). Synthetic data is often used for training Machine Learning (ML) models, testing applications, and validating systems at scale.
It’s important to understand the difference between production data and synthetic data. Production data is real data that is collected from actual sources, and is typically used for operational purposes, like running applications or training ML models. In contrast, synthetic data is generated by algorithms to imitate the statistical properties of real data, and is used for testing applications, analytics, or research purposes.
Methods of Data Synthesis
Data synthesis, as it relates to synthetic data, involves the generation of realistic datasets that could pass for production data. There are 2 primary ways to synthesize data:
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Rule-based data generation
Rule-based data generation allows users to define the schema of the dataset they want to create, which the system then generates based on predefined rules. This method often involves randomly generating values using open-source libraries and tools. By specifying the desired fields and their associated types and relationships, users can generate synthetic datasets that adhere to certain specifications. For instance, a synthetic dataset of university students might include student names, genders, birthdates, addresses, email addresses, and fields of study.
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Deep generative models
Deep Machine Learning (ML) generative models, such as Generative Adversarial Networks (GANs) and Generative Pre-trained Transformer (GPT) methodology, have gained prominence in generating high-quality fake data. GANs are composed of a "generator" that creates realistic synthetic data, and a "discriminator" that distinguishes between real and fabricated data points. Based on a language model, GPT can be used to generate realistic, but fake, text and other forms of tabular data.
Differential Privacy: Enhancing Privacy in Synthetic Data
To further protect privacy in synthetic data, the concept of differential privacy can be applied to both rule-based data generation and deep generative models. Differential privacy adds statistical noise to datasets, making it difficult for malicious actors to specify records. It also allows for data de-identification and re-identification to strengthen the security of synthetic outputs – ensuring that individual privacy is maintained while analyzing aggregated datasets.
Data Anonymization vs Synthetic Data Generation
Data anonymization removes identifying information from data so that it cannot be linked back to individuals. This can be done by removing names, addresses, and other PII. Anonymized data can still be used for research and analysis, but it cannot be used to identify individuals.
Synthetic data generation creates fake data that could be mistaken for real-world data. Synthetic data is created using user-defined rules or ML algorithms that learn from real data, so it retains the statistical properties of real data while also protecting privacy.
The main difference between data anonymization and synthetic data generation is that anonymized data is derived from real data, while synthetic data is entirely made up. So while synthetic data may provide more privacy than anonymized data, it may not be as accurate.
Synthetic Data Generation Techniques
Enterprises synthesize data to maintain data confidentiality and adhere to data retention best practices. As businesses store and process vast amounts of data, the need to protect sensitive information and comply with privacy regulations is critical. De-identification techniques, such as synthetic data generation, can effectively remove direct identifiers and alter other information to prevent re-identification. By implementing controls and safeguards in the data access environment, organizations can further prevent re-identification and meet privacy obligations while building trust in their data governance practices.
Various synthetic data generation techniques can be employed depending on the use case:
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Synthetic Minority Over-sampling Technique (SMOTE)
SMOTE is useful when dealing with incomplete or imbalanced datasets. It generates synthetic instances to balance the class distribution. -
ADAptive SYNthetic (ADASYN) sampling method
Similar to SMOTE, ADASYN adapts to the lack of data or well-known categories within the data. It focuses on generating synthetic samples in areas with a smaller number of instances, helping to balance the dataset. -
Data augmentation
Data augmentation involves modifying existing datasets to increase the number of cases. This technique is particularly useful for training ML models, enabling the generation of additional instances for better model performance. By augmenting the data, organizations can expand the diversity and volume of their training data. -
Variational Auto-Encoder (VAE)
A VAE converts data into codes based on a specific distribution. It follows the distribution of the original data, preserving statistical properties.
Benefits of Synthesizing Data
Synthesizing data offers various benefits, including:
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Safeguarding privacy
Synthetic data acts as a filter for information that would otherwise compromise the confidentiality of sensitive aspects of the data. It enables organizations to perform testing and development activities without exposing real personal information. -
Testing in lower environments
Synthetic data is useful for refreshing databases from production into lower environments, ensuring data anonymity. It facilitates the creation of new environments and supports repeated testing rounds. -
Training ML models
Synthetic data has become increasingly popular for training machine learning models. It offers advantages such as the ability to generate new datasets easily, completing categories without synthetic sampling, and serving as a perfect substitute for sensitive datasets.
Synthesize Data with a Business Entity Approach
Synthesizing data that accurately mimics the real world is a complex task. Ensuring that the synthetic dataset retains the statistical characteristics and variability of the original data is crucial for reliable analysis and modeling, but doing so at scale comes with its own challenges.
Enterprises are now turning to entity-based synthetic data technology because it generates highly
realistic but artificial data whose referential integrity is enforced in the target systems. Business
entities (such as customer, device, order, etc.) are automatically modeled based on metadata from the
source systems.
The business entity model serves as a blueprint on how to generate fake data. Entity-based synthetic data generation supports a variety of different data generation techniques (used alone or together) to create artificial data for AI/ML modeling and software testing. This can include AI-based generation, rule-based generation, cloning, and data masking. Only entity-based synthetic data generation tools support all these techniques.
