Motivation

This dataset was created to pilot techniques for creating synthetic data from datasets containing sensitive and protected information in the local government context. Synthetic data generation replaces actual data with representative data generated from statistical models; this preserves the key data properties that allow insights to be drawn from the data while protecting the privacy of the people included in the data. We invite you to read the Understanding Synthetic Data white paper for a concise introduction to synthetic data.

This effort was a collaboration of the Urban Institute, Allegheny County’s Department of Human Services (DHS) and CountyStat, and the University of Pittsburgh’s Western Pennsylvania Regional Data Center.

Collection

The source data for this project consisted of 1) month-by-month records of services included in Allegheny County's data warehouse and 2) demographic data about the individuals who received the services. As the County’s data warehouse combines this service and client data, this data is referred to as “Integrated Services data”. Read more about the data warehouse and the kinds of services it includes here.

Preprocessing

Synthetic data are typically generated from probability distributions or models identified as being representative of the confidential data. For this dataset, a model of the Integrated Services data was used to generate multiple versions of the synthetic dataset. These different candidate datasets were evaluated to select for publication the dataset version that best balances utility and privacy. For high-level information about this evaluation, see the Synthetic Data User Guide.

For more information about the creation of the synthetic version of this data, see the technical brief for this project, which discusses the technical decision making and modeling process in more detail.

Recommended Uses

This disaggregated synthetic data allows for many analyses that are not possible with aggregate data (summary statistics). Broadly, this synthetic version of this data could be analyzed to better understand the usage of human services by people in Allegheny County, including the interplay in the usage of multiple services and demographic information about clients.

Known Limitations/Biases

Some amount of deviation from the original data is inherent to the synthetic data generation process. Specific examples of limitations (including undercounts and overcounts for the usage of different services) are given in the Synthetic Data User Guide and the technical report describing this dataset's creation.

Feedback

Please reach out to this dataset's data steward (listed below) to let us know how you are using this data and if you found it to be helpful. Please also provide any feedback on how to make this dataset more applicable to your work, any suggestions of future synthetic datasets, or any additional information that would make this more useful. Also, please copy wprdc@pitt.edu on any such feedback (as the WPRDC always loves to hear about how people use the data that they publish and how the data could be improved).

Further Documentation and Resources

1) A high-level overview of synthetic data generation as a method for protecting privacy can be found in the Understanding Synthetic Data white paper.
2) The Synthetic Data User Guide provides high-level information to help users understand the motivation, evaluation process, and limitations of the synthetic version of Allegheny County DHS's Human Services data published here.
3) Generating a Fully Synthetic Human Services Dataset: A Technical Report on Synthesis and Evaluation Methodologies describes the full technical methodology used for generating the synthetic data, evaluating the various options, and selecting the final candidate for publication.
4) The WPRDC also hosts the Allegheny County Human Services Community Profiles dataset, which provides annual updates on human-services usage, aggregated by neighborhood/municipality. That data can be explored using the County's Human Services Community Profile web site.

Dataset used in the article entitled 'Synthetic Datasets Generator for Testing Information Visualization and Machine Learning Techniques and Tools'. These datasets can be used to test several characteristics in machine learning and data processing algorithms.

Synthetic Data Software Market Outlook

The global synthetic data software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 7.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 22.4% during the forecast period. The growth of this market can be attributed to the increasing demand for data privacy and security, advancements in artificial intelligence (AI) and machine learning (ML), and the rising need for high-quality data to train AI models.

One of the primary growth factors for the synthetic data software market is the escalating concern over data privacy and governance. With the rise of stringent data protection regulations like GDPR in Europe and CCPA in California, organizations are increasingly seeking alternatives to real data that can still provide meaningful insights without compromising privacy. Synthetic data software offers a solution by generating artificial data that mimics real-world data distributions, thereby mitigating privacy risks while still allowing for robust data analysis and model training.

Another significant driver of market growth is the rapid advancement in AI and ML technologies. These technologies require vast amounts of data to train models effectively. Traditional data collection methods often fall short in terms of volume, variety, and veracity. Synthetic data software addresses these limitations by creating scalable, diverse, and accurate datasets, enabling more effective and efficient model training. As AI and ML applications continue to expand across various industries, the demand for synthetic data software is expected to surge.

The increasing application of synthetic data software across diverse sectors such as healthcare, finance, automotive, and retail also acts as a catalyst for market growth. In healthcare, synthetic data can be used to simulate patient records for research without violating patient privacy laws. In finance, it can help in creating realistic datasets for fraud detection and risk assessment without exposing sensitive financial information. Similarly, in automotive, synthetic data is crucial for training autonomous driving systems by simulating various driving scenarios.

From a regional perspective, North America holds the largest market share due to its early adoption of advanced technologies and the presence of key market players. Europe follows closely, driven by stringent data protection regulations and a strong focus on privacy. The Asia Pacific region is expected to witness the highest growth rate owing to the rapid digital transformation, increasing investments in AI and ML, and a burgeoning tech-savvy population. Latin America and the Middle East & Africa are also anticipated to experience steady growth, supported by emerging technological ecosystems and increasing awareness of data privacy.

Component Analysis

When examining the synthetic data software market by component, it is essential to consider both software and services. The software segment dominates the market as it encompasses the actual tools and platforms that generate synthetic data. These tools leverage advanced algorithms and statistical methods to produce artificial datasets that closely resemble real-world data. The demand for such software is growing rapidly as organizations across various sectors seek to enhance their data capabilities without compromising on security and privacy.

On the other hand, the services segment includes consulting, implementation, and support services that help organizations integrate synthetic data software into their existing systems. As the market matures, the services segment is expected to grow significantly. This growth can be attributed to the increasing complexity of synthetic data generation and the need for specialized expertise to optimize its use. Service providers offer valuable insights and best practices, ensuring that organizations maximize the benefits of synthetic data while minimizing risks.

The interplay between software and services is crucial for the holistic growth of the synthetic data software market. While software provides the necessary tools for data generation, services ensure that these tools are effectively implemented and utilized. Together, they create a comprehensive solution that addresses the diverse needs of organizations, from initial setup to ongoing maintenance and support. As more organizations recognize the value of synthetic data, the demand for both software and services is expected to rise, driving overall market growth.

BackgroundClinical data is instrumental to medical research, machine learning (ML) model development, and advancing surgical care, but access is often constrained by privacy regulations and missing data. Synthetic data offers a promising solution to preserve privacy while enabling broader data access. Recent advances in large language models (LLMs) provide an opportunity to generate synthetic data with reduced reliance on domain expertise, computational resources, and pre-training.ObjectiveThis study aims to assess the feasibility of generating realistic tabular clinical data with OpenAI’s GPT-4o using zero-shot prompting, and evaluate the fidelity of LLM-generated data by comparing its statistical properties to the Vital Signs DataBase (VitalDB), a real-world open-source perioperative dataset.MethodsIn Phase 1, GPT-4o was prompted to generate a dataset with qualitative descriptions of 13 clinical parameters. The resultant data was assessed for general errors, plausibility of outputs, and cross-verification of related parameters. In Phase 2, GPT-4o was prompted to generate a dataset using descriptive statistics of the VitalDB dataset. Fidelity was assessed using two-sample t-tests, two-sample proportion tests, and 95% confidence interval (CI) overlap.ResultsIn Phase 1, GPT-4o generated a complete and structured dataset comprising 6,166 case files. The dataset was plausible in range and correctly calculated body mass index for all case files based on respective heights and weights. Statistical comparison between the LLM-generated datasets and VitalDB revealed that Phase 2 data achieved significant fidelity. Phase 2 data demonstrated statistical similarity in 12/13 (92.31%) parameters, whereby no statistically significant differences were observed in 6/6 (100.0%) categorical/binary and 6/7 (85.71%) continuous parameters. Overlap of 95% CIs were observed in 6/7 (85.71%) continuous parameters.ConclusionZero-shot prompting with GPT-4o can generate realistic tabular synthetic datasets, which can replicate key statistical properties of real-world perioperative data. This study highlights the potential of LLMs as a novel and accessible modality for synthetic data generation, which may address critical barriers in clinical data access and eliminate the need for technical expertise, extensive computational resources, and pre-training. Further research is warranted to enhance fidelity and investigate the use of LLMs to amplify and augment datasets, preserve multivariate relationships, and train robust ML models.

Data Description

We release the synthetic data generated using the method described in the paper Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models (ACL 2024 Findings). The external knowledge we use is based on LLM-generated topics and writing styles.

  Generated Datasets

The original train/validation/test data, and the generated synthetic training data are listed as follows. For each dataset, we generate 5000… See the full description on the dataset page: https://huggingface.co/datasets/ritaranx/clinical-synthetic-text-llm.

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Synthetic Data Generation Market Size 2025-2029

The synthetic data generation market size is forecast to increase by USD 4.39 billion, at a CAGR of 61.1% between 2024 and 2029.

The market is experiencing significant growth, driven by the escalating demand for data privacy protection. With increasing concerns over data security and the potential risks associated with using real data, synthetic data is gaining traction as a viable alternative. Furthermore, the deployment of large language models is fueling market expansion, as these models can generate vast amounts of realistic and diverse data, reducing the reliance on real-world data sources. However, high costs associated with high-end generative models pose a challenge for market participants. These models require substantial computational resources and expertise to develop and implement effectively. Companies seeking to capitalize on market opportunities must navigate these challenges by investing in research and development to create more cost-effective solutions or partnering with specialists in the field. Overall, the market presents significant potential for innovation and growth, particularly in industries where data privacy is a priority and large language models can be effectively utilized.

What will be the Size of the Synthetic Data Generation Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the increasing demand for data-driven insights across various sectors. Data processing is a crucial aspect of this market, with a focus on ensuring data integrity, privacy, and security. Data privacy-preserving techniques, such as data masking and anonymization, are essential in maintaining confidentiality while enabling data sharing. Real-time data processing and data simulation are key applications of synthetic data, enabling predictive modeling and data consistency. Data management and workflow automation are integral components of synthetic data platforms, with cloud computing and model deployment facilitating scalability and flexibility. Data governance frameworks and compliance regulations play a significant role in ensuring data quality and security. Deep learning models, variational autoencoders (VAEs), and neural networks are essential tools for model training and optimization, while API integration and batch data processing streamline the data pipeline. Machine learning models and data visualization provide valuable insights, while edge computing enables data processing at the source. Data augmentation and data transformation are essential techniques for enhancing the quality and quantity of synthetic data. Data warehousing and data analytics provide a centralized platform for managing and deriving insights from large datasets. Synthetic data generation continues to unfold, with ongoing research and development in areas such as federated learning, homomorphic encryption, statistical modeling, and software development. The market's dynamic nature reflects the evolving needs of businesses and the continuous advancements in data technology.

How is this Synthetic Data Generation Industry segmented?

The synthetic data generation industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userHealthcare and life sciencesRetail and e-commerceTransportation and logisticsIT and telecommunicationBFSI and othersTypeAgent-based modellingDirect modellingApplicationAI and ML Model TrainingData privacySimulation and testingOthersProductTabular dataText dataImage and video dataOthersGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalyUKAPACChinaIndiaJapanRest of World (ROW)

By End-user Insights

The healthcare and life sciences segment is estimated to witness significant growth during the forecast period.In the rapidly evolving data landscape, the market is gaining significant traction, particularly in the healthcare and life sciences sector. With a growing emphasis on data-driven decision-making and stringent data privacy regulations, synthetic data has emerged as a viable alternative to real data for various applications. This includes data processing, data preprocessing, data cleaning, data labeling, data augmentation, and predictive modeling, among others. Medical imaging data, such as MRI scans and X-rays, are essential for diagnosis and treatment planning. However, sharing real patient data for research purposes or training machine learning algorithms can pose significant privacy risks. Synthetic data generation addresses this challenge by producing realistic medical imaging data, ensuring data privacy while enabling research

Abstract

The dataset is a relational dataset of 8,000 households households, representing a sample of the population of an imaginary middle-income country. The dataset contains two data files: one with variables at the household level, the other one with variables at the individual level. It includes variables that are typically collected in population censuses (demography, education, occupation, dwelling characteristics, fertility, mortality, and migration) and in household surveys (household expenditure, anthropometric data for children, assets ownership). The data only includes ordinary households (no community households). The dataset was created using REaLTabFormer, a model that leverages deep learning methods. The dataset was created for the purpose of training and simulation and is not intended to be representative of any specific country.

The full-population dataset (with about 10 million individuals) is also distributed as open data.

Geographic coverage

The dataset is a synthetic dataset for an imaginary country. It was created to represent the population of this country by province (equivalent to admin1) and by urban/rural areas of residence.

Analysis unit

Household, Individual

Universe

The dataset is a fully-synthetic dataset representative of the resident population of ordinary households for an imaginary middle-income country.

Kind of data

ssd

Sampling procedure

The sample size was set to 8,000 households. The fixed number of households to be selected from each enumeration area was set to 25. In a first stage, the number of enumeration areas to be selected in each stratum was calculated, proportional to the size of each stratum (stratification by geo_1 and urban/rural). Then 25 households were randomly selected within each enumeration area. The R script used to draw the sample is provided as an external resource.

Mode of data collection

other

Research instrument

The dataset is a synthetic dataset. Although the variables it contains are variables typically collected from sample surveys or population censuses, no questionnaire is available for this dataset. A "fake" questionnaire was however created for the sample dataset extracted from this dataset, to be used as training material.

Cleaning operations

The synthetic data generation process included a set of "validators" (consistency checks, based on which synthetic observation were assessed and rejected/replaced when needed). Also, some post-processing was applied to the data to result in the distributed data files.

Response rate

This is a synthetic dataset; the "response rate" is 100%.

Test Data Generation Tools Market Outlook

The global market size for Test Data Generation Tools was valued at USD 800 million in 2023 and is projected to reach USD 2.2 billion by 2032, growing at a CAGR of 12.1% during the forecast period. The surge in the adoption of agile and DevOps practices, along with the increasing complexity of software applications, is driving the growth of this market.

One of the primary growth factors for the Test Data Generation Tools market is the increasing need for high-quality test data in software development. As businesses shift towards more agile and DevOps methodologies, the demand for automated and efficient test data generation solutions has surged. These tools help in reducing the time required for test data creation, thereby accelerating the overall software development lifecycle. Additionally, the rise in digital transformation across various industries has necessitated the need for robust testing frameworks, further propelling the market growth.

The proliferation of big data and the growing emphasis on data privacy and security are also significant contributors to market expansion. With the introduction of stringent regulations like GDPR and CCPA, organizations are compelled to ensure that their test data is compliant with these laws. Test Data Generation Tools that offer features like data masking and data subsetting are increasingly being adopted to address these compliance requirements. Furthermore, the increasing instances of data breaches have underscored the importance of using synthetic data for testing purposes, thereby driving the demand for these tools.

Another critical growth factor is the technological advancements in artificial intelligence and machine learning. These technologies have revolutionized the field of test data generation by enabling the creation of more realistic and comprehensive test data sets. Machine learning algorithms can analyze large datasets to generate synthetic data that closely mimics real-world data, thus enhancing the effectiveness of software testing. This aspect has made AI and ML-powered test data generation tools highly sought after in the market.

Regional outlook for the Test Data Generation Tools market shows promising growth across various regions. North America is expected to hold the largest market share due to the early adoption of advanced technologies and the presence of major software companies. Europe is also anticipated to witness significant growth owing to strict regulatory requirements and increased focus on data security. The Asia Pacific region is projected to grow at the highest CAGR, driven by rapid industrialization and the growing IT sector in countries like India and China.

Synthetic Data Generation has emerged as a pivotal component in the realm of test data generation tools. This process involves creating artificial data that closely resembles real-world data, without compromising on privacy or security. The ability to generate synthetic data is particularly beneficial in scenarios where access to real data is restricted due to privacy concerns or regulatory constraints. By leveraging synthetic data, organizations can perform comprehensive testing without the risk of exposing sensitive information. This not only ensures compliance with data protection regulations but also enhances the overall quality and reliability of software applications. As the demand for privacy-compliant testing solutions grows, synthetic data generation is becoming an indispensable tool in the software development lifecycle.

Component Analysis

The Test Data Generation Tools market is segmented into software and services. The software segment is expected to dominate the market throughout the forecast period. This dominance can be attributed to the increasing adoption of automated testing tools and the growing need for robust test data management solutions. Software tools offer a wide range of functionalities, including data profiling, data masking, and data subsetting, which are essential for effective software testing. The continuous advancements in software capabilities also contribute to the growth of this segment.

In contrast, the services segment, although smaller in market share, is expected to grow at a substantial rate. Services include consulting, implementation, and support services, which are crucial for the successful deployment and management of test data generation tools. The increasing complexity of IT inf

In this research, we create synthetic data with features that are like data from IoT devices. We use an existing air quality dataset that includes temperature and gas sensor measurements. This real-time dataset includes component values for the Air Quality Index (AQI) and ppm concentrations for various polluting gas concentrations. We build a JavaScript Object Notation (JSON) model to capture the distribution of variables and structure of this real dataset to generate the synthetic data. Based on the synthetic dataset and original dataset, we create a comparative predictive model. Analysis of synthetic dataset predictive model shows that it can be successfully used for edge analytics purposes, replacing real-world datasets. There is no significant difference between the real-world dataset compared the synthetic dataset. The generated synthetic data requires no modification to suit the edge computing requirements. The framework can generate correct synthetic datasets based on JSON schema attributes. The accuracy, precision, and recall values for the real and synthetic datasets indicate that the logistic regression model is capable of successfully classifying data

SynthPAI was created to provide a dataset that can be used to investigate the personal attribute inference (PAI) capabilities of LLM on online texts. Due to associated privacy concerns with real-world data, open datasets are rare (non-existent) in the research community. SynthPAI is a synthetic dataset that aims to fill this gap.

Dataset Details Dataset Description SynthPAI was created using 300 GPT-4 agents seeded with individual personalities interacting with each other in a simulated online forum and consists of 103 threads and 7823 comments. For each profile, we further provide a set of personal attributes that a human could infer from the profile. We additionally conducted a user study to evaluate the quality of the synthetic comments, establishing that humans can barely distinguish between real and synthetic comments.

Curated by: The dataset was created by SRILab at ETH Zurich. It was not created on behalf of any outside entity. Funded by: Two authors of this work are supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) (SERI-funded ERC Consolidator Grant). This project did, however, not receive explicit funding by SERI and was devised independently. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the SERI-funded ERC Consolidator Grant. Shared by: SRILab at ETH Zurich Language(s) (NLP): English License: CC-BY-NC-SA-4.0

Dataset Sources

Repository: https://github.com/eth-sri/SynthPAI Paper: https://arxiv.org/abs/2406.07217

Uses The dataset is intended to be used as a privacy-preserving method of (i) evaluating PAI capabilities of language models and (ii) aiding the development of potential defenses against such automated inferences.

Direct Use As in the associated paper , where we include an analysis of the personal attribute inference (PAI) capabilities of 18 state-of-the-art LLMs across different attributes and on anonymized texts.

Out-of-Scope Use The dataset shall not be used as part of any system that performs attribute inferences on real natural persons without their consent or otherwise maliciously.

Dataset Structure We provide the instance descriptions below. Each data point consists of a single comment (that can be a top-level post):

Comment

author str: unique identifier of the person writing

username str: corresponding username

parent_id str: unique identifier of the parent comment

thread_id str: unique identifier of the thread

children list[str]: unique identifiers of children comments

profile Profile: profile making the comment - described below

text str: text of the comment

guesses list[dict]: Dict containing model estimates of attributes based on the comment. Only contains attributes for which a prediction exists.

reviews dict: Dict containing human estimates of attributes based on the comment. Each guess contains a corresponding hardness rating (and certainty rating). Contains all attributes

The associated profiles are structured as follows

Profile

username str: identifier

attributes: set of personal attributes that describe the user (directly listed below)

The corresponding attributes and values are

Attributes

Age continuous [18-99] The age of a user in years.

Place of Birth tuple [city, country] The place of birth of a user. We create tuples jointly for city and country in free-text format. (field name: birth_city_country)

Location tuple [city, country] The current location of a user. We create tuples jointly for city and country in free-text format. (field name: city_country)

Education free-text We use a free-text field to describe the user's education level. This includes additional details such as the degree and major. To ensure comparability with the evaluation of prior work, we later map these to a categorical scale: high school, college degree, master's degree, PhD.

Income Level free-text [low, medium, high, very high] The income level of a user. We first generate a continuous income level in the profile's local currency. In our code, we map this to a categorical value considering the distribution of income levels in the respective profile location. For this, we roughly follow the local equivalents of the following reference levels for the US: Low (<30k USD), Middle (30-60k USD), High (60-150k USD), Very High (>150k USD).

Occupation free-text The occupation of a user, described as a free-text field.

Relationship Status categorical [single, In a Relationship, married, divorced, widowed] The relationship status of a user as one of 5 categories.

Sex categorical [Male, Female] Biological Sex of a profile.

Dataset Creation Curation Rationale SynthPAI was created to provide a dataset that can be used to investigate the personal attribute inference (PAI) capabilities of LLM on online texts. Due to associated privacy concerns with real-world data, open datasets are rare (non-existent) in the research community. SynthPAI is a synthetic dataset that aims to fill this gap. We additionally conducted a user study to evaluate the quality of the synthetic comments, establishing that humans can barely distinguish between real and synthetic comments.

Source Data The dataset is fully synthetic and was created using GPT-4 agents (version gpt-4-1106-preview) seeded with individual personalities interacting with each other in a simulated online forum.

Data Collection and Processing The dataset was created by sampling comments from the agents in threads. A human then inferred a set of personal attributes from sets of comments associated with each profile. Further, it was manually reviewed to remove any offensive or inappropriate content. We give a detailed overview of our dataset-creation procedure in the corresponding paper.

Annotations

Annotations are provided by authors of the paper.

Personal and Sensitive Information

All contained personal information is purely synthetic and does not relate to any real individual.

Bias, Risks, and Limitations All profiles are synthetic and do not correspond to any real subpopulations. We provide a distribution of the personal attributes of the profiles in the accompanying paper. As the dataset has been created synthetically, data points can inherit limitations (e.g., biases) from the underlying model, GPT-4. While we manually reviewed comments individually, we cannot provide respective guarantees.

Citation BibTeX:

@misc{2406.07217, Author = {Hanna Yukhymenko and Robin Staab and Mark Vero and Martin Vechev}, Title = {A Synthetic Dataset for Personal Attribute Inference}, Year = {2024}, Eprint = {arXiv:2406.07217}, } APA:

Hanna Yukhymenko, Robin Staab, Mark Vero, Martin Vechev: “A Synthetic Dataset for Personal Attribute Inference”, 2024; arXiv:2406.07217.

Dataset Card Authors

Hanna Yukhymenko Robin Staab Mark Vero

Synthetic Data Generation Market Outlook 2032

The global synthetic data generation market size was USD 378.3 Billion in 2023 and is projected to reach USD 13,800 Billion by 2032, expanding at a CAGR of 31.1 % during 2024–2032. The market growth is attributed to the increasing demand for privacy-preserving synthetic data across the world.



Growing demand for privacy-preserving synthetic data is expected to boost the market. Synthetic data, being artificially generated, does not contain any personal or sensitive information, thereby ensuring data privacy. This has propelled organizations to adopt synthetic data generation methods, particularly in sectors where data privacy is paramount, such as healthcare and finance.

Impact of Artificial Intelligence (AI) in Synthetic Data Generation Market

Artificial Intelligence (AI) has significantly influenced the synthetic data generation market, transforming the way businesses operate and make decisions. The integration of AI in synthetic data generation has enhanced the efficiency and accuracy of data modeling, simulation, and analysis. AI algorithms, through machine learning and deep learning techniques, generate synthetic data that closely mimics real-world data, thereby providing a safe and effective alternative for data privacy concerns.



AI has led to the increased adoption of synthetic data in various sectors such as healthcare, finance, and retail, among others. Furthermore, AI-driven synthetic data generation aids in overcoming the challenges of data scarcity and bias, thereby improving the quality of predictive models and decision-making processes. The impact of AI on the synthetic data generation market is profound, fostering innovation, enhancing data security, and driving market growth. For instance,

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  In October 2023, K2view

Title: Rule-based Synthetic Data for Japanese GEC. Dataset Contents:This dataset contains two parallel corpora intended for the training and evaluating of models for the NLP (natural language processing) subtask of Japanese GEC (grammatical error correction). These are as follows:Synthetic Corpus - synthesized_data.tsv. This corpus file contains 2,179,130 parallel sentence pairs synthesized using the process described in [1]. Each line of the file consists of two sentences delimited by a tab. The first sentence is the erroneous sentence while the second is the corresponding correction.These paired sentences are derived from data scraped from the keyword-lookup site

Surveillance data play a vital role in estimating the burden of diseases, pathogens, exposures, behaviors, and susceptibility in populations, providing insights that can inform the design of policies and targeted public health interventions. The use of Health and Demographic Surveillance System (HDSS) collected from the Kilifi region of Kenya, has led to the collection of massive amounts of data on the demographics and health events of different populations. This has necessitated the adoption of tools and techniques to enhance data analysis to derive insights that will improve the accuracy and efficiency of decision-making. Machine Learning (ML) and artificial intelligence (AI) based techniques are promising for extracting insights from HDSS data, given their ability to capture complex relationships and interactions in data. However, broad utilization of HDSS datasets using AI/ML is currently challenging as most of these datasets are not AI-ready due to factors that include, but are not limited to, regulatory concerns around privacy and confidentiality, heterogeneity in data laws across countries limiting the accessibility of data, and a lack of sufficient datasets for training AI/ML models. Synthetic data generation offers a potential strategy to enhance accessibility of datasets by creating synthetic datasets that uphold privacy and confidentiality, suitable for training AI/ML models and can also augment existing AI datasets used to train the AI/ML models. These synthetic datasets, generated from two rounds of separate data collection periods, represent a version of the real data while retaining the relationships inherent in the data. For more information please visit The Aga Khan University Website.

The size of the Synthetic Data Generation Market market was valued at USD 45.9 billion in 2023 and is projected to reach USD 65.9 billion by 2032, with an expected CAGR of 13.6 % during the forecast period. The Synthetic Data Generation Market involves creating artificial data that mimics real-world data while preserving privacy and security. This technique is increasingly used in various industries, including finance, healthcare, and autonomous vehicles, to train machine learning models without compromising sensitive information. Synthetic data is utilized for testing algorithms, improving AI models, and enhancing data analysis processes. Key trends in this market include the growing demand for privacy-compliant data solutions, advancements in generative modeling techniques, and increased investment in AI technologies. As organizations seek to leverage data-driven insights while mitigating risks associated with data privacy, the synthetic data generation market is poised for significant growth in the coming years.

Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets. However, biomechanical data are frequently limited due to diverse challenges. Effective methods for augmenting data in developing ML models, specifically in the human posture domain, are scarce. Therefore, this study explored the feasibility of leveraging generative artificial intelligence (AI) to produce realistic synthetic posture data by utilizing three-dimensional posture data.Methods: Data were collected from 338 subjects through surface topography. A Variational Autoencoder (VAE) architecture was employed to generate and evaluate synthetic posture data, examining its distinguishability from real data by domain experts, ML classifiers, and Statistical Parametric Mapping (SPM). The benefits of incorporating augmented posture data into the learning process were exemplified by a deep autoencoder (AE) for automated feature representation.Results: Our findings highlight the challenge of differentiating synthetic data from real data for both experts and ML classifiers, underscoring the quality of synthetic data. This observation was also confirmed by SPM. By integrating synthetic data into AE training, the reconstruction error can be reduced compared to using only real data samples. Moreover, this study demonstrates the potential for reduced latent dimensions, while maintaining a reconstruction accuracy comparable to AEs trained exclusively on real data samples.Conclusion: This study emphasizes the prospects of harnessing generative AI to enhance ML tasks in the biomechanics domain.

Synthetic Data Generation Engine Market Outlook

According to our latest research, the global Synthetic Data Generation Engine market size reached USD 1.42 billion in 2024, reflecting a rapidly expanding sector driven by the escalating demand for advanced data solutions. The market is expected to achieve a robust CAGR of 37.8% from 2025 to 2033, propelling it to an estimated value of USD 21.8 billion by 2033. This exceptional growth is primarily fueled by the increasing need for high-quality, privacy-compliant datasets to train artificial intelligence and machine learning models in sectors such as healthcare, BFSI, and IT & telecommunications. As per our latest research, the proliferation of data-centric applications and stringent data privacy regulations are acting as significant catalysts for the adoption of synthetic data generation engines globally.

One of the key growth factors for the synthetic data generation engine market is the mounting emphasis on data privacy and compliance with regulations such as GDPR and CCPA. Organizations are under immense pressure to protect sensitive customer information while still deriving actionable insights from data. Synthetic data generation engines offer a compelling solution by creating artificial datasets that mimic real-world data without exposing personally identifiable information. This not only ensures compliance but also enables organizations to accelerate their AI and analytics initiatives without the constraints of data access or privacy risks. The rising awareness among enterprises about the benefits of synthetic data in mitigating data breaches and regulatory penalties is further propelling market expansion.

Another significant driver is the exponential growth in artificial intelligence and machine learning adoption across industries. Training robust and unbiased models requires vast and diverse datasets, which are often difficult to obtain due to privacy concerns, labeling costs, or data scarcity. Synthetic data generation engines address this challenge by providing scalable and customizable datasets for various applications, including machine learning model training, data augmentation, and fraud detection. The ability to generate balanced and representative data has become a critical enabler for organizations seeking to improve model accuracy, reduce bias, and accelerate time-to-market for AI solutions. This trend is particularly pronounced in sectors such as healthcare, automotive, and finance, where data diversity and privacy are paramount.

Furthermore, the increasing complexity of data types and the need for multi-modal data synthesis are shaping the evolution of the synthetic data generation engine market. With the proliferation of unstructured data in the form of images, videos, audio, and text, organizations are seeking advanced engines capable of generating synthetic data across multiple modalities. This capability enhances the versatility of synthetic data solutions, enabling their application in emerging use cases such as autonomous vehicle simulation, natural language processing, and biometric authentication. The integration of generative AI techniques, such as GANs and diffusion models, is further enhancing the realism and utility of synthetic datasets, expanding the addressable market for synthetic data generation engines.

From a regional perspective, North America continues to dominate the synthetic data generation engine market, accounting for the largest revenue share in 2024. The region's leadership is attributed to the strong presence of technology giants, early adoption of AI and machine learning, and stringent regulatory frameworks. Europe follows closely, driven by robust data privacy regulations and increasing investments in digital transformation. Meanwhile, the Asia Pacific region is emerging as the fastest-growing market, supported by expanding IT infrastructure, government-led AI initiatives, and a burgeoning startup ecosystem. Latin America and the Middle East & Africa are also witnessing gradual adoption, fueled by the growing recognition of synthetic data's potential to overcome data access and privacy challenges.

Synthetic dataset:

generated.csv - synthetic datasets containing 41,185 clinical note samples spanning 219 ICD-10 codes.

Data field Description

idx Unique sample identifier.

ICD-10 The targeted ICD-10 code used for prior data sampling.

generation_model The model used for sample generation (GTP-3.5, GPT-4, LLaMA-7b, LLaMA-13b)

prompt Prompt used for sample generation.

prior Type of prior data used for sample generation.

example Bool variable for the presence or… See the full description on the dataset page: https://huggingface.co/datasets/Glebkaa/MedSyn-synthetic.

Synthetic Data Generation Market Outlook

According to our latest research, the global synthetic data generation market size reached USD 1.6 billion in 2024, demonstrating robust expansion driven by increasing demand for high-quality, privacy-preserving datasets. The market is projected to grow at a CAGR of 38.2% over the forecast period, reaching USD 19.2 billion by 2033. This remarkable growth trajectory is fueled by the growing adoption of artificial intelligence (AI) and machine learning (ML) technologies across industries, coupled with stringent data privacy regulations that necessitate innovative data solutions. As per our latest research, organizations worldwide are increasingly leveraging synthetic data to address data scarcity, enhance AI model training, and ensure compliance with evolving privacy standards.

One of the primary growth factors for the synthetic data generation market is the rising emphasis on data privacy and regulatory compliance. With the implementation of stringent data protection laws such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, enterprises are under immense pressure to safeguard sensitive information. Synthetic data offers a compelling solution by enabling organizations to generate artificial datasets that mirror the statistical properties of real data without exposing personally identifiable information. This not only facilitates regulatory compliance but also empowers organizations to innovate without the risk of data breaches or privacy violations. As businesses increasingly recognize the value of privacy-preserving data, the demand for advanced synthetic data generation solutions is set to surge.

Another significant driver is the exponential growth in AI and ML adoption across various sectors, including healthcare, finance, automotive, and retail. High-quality, diverse, and unbiased data is the cornerstone of effective AI model development. However, acquiring such data is often challenging due to privacy concerns, limited availability, or high acquisition costs. Synthetic data generation bridges this gap by providing scalable, customizable datasets tailored to specific use cases, thereby accelerating AI training and reducing dependency on real-world data. Organizations are leveraging synthetic data to enhance algorithm performance, mitigate data bias, and simulate rare events, which are otherwise difficult to capture in real datasets. This capability is particularly valuable in sectors like autonomous vehicles, where training models on rare but critical scenarios is essential for safety and reliability.

Furthermore, the growing complexity of data types—ranging from tabular and image data to text, audio, and video—has amplified the need for versatile synthetic data generation tools. Enterprises are increasingly seeking solutions that can generate multi-modal synthetic datasets to support diverse applications such as fraud detection, product testing, and quality assurance. The flexibility offered by synthetic data generation platforms enables organizations to simulate a wide array of scenarios, test software systems, and validate AI models in controlled environments. This not only enhances operational efficiency but also drives innovation by enabling rapid prototyping and experimentation. As the digital ecosystem continues to evolve, the ability to generate synthetic data across various formats will be a critical differentiator for businesses striving to maintain a competitive edge.

Regionally, North America leads the synthetic data generation market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the strong presence of technology giants, advanced research institutions, and a favorable regulatory environment that encourages AI innovation. Europe is witnessing rapid growth due to proactive data privacy regulations and increasing investments in digital transformation initiatives. Meanwhile, Asia Pacific is emerging as a high-growth region, driven by the proliferation of digital technologies and rising adoption of AI-powered solutions across industries. Latin America and the Middle East & Africa are also expected to experience steady growth, supported by government-led digitalization programs and expanding IT infrastructure.

Synthetic Data Generation Engine Market Outlook

According to our latest research, the global synthetic data generation engine market size reached USD 1.48 billion in 2024. The market is experiencing robust expansion, driven by the increasing demand for privacy-compliant data and advanced analytics solutions. The market is projected to grow at a remarkable CAGR of 35.6% from 2025 to 2033, reaching an estimated USD 18.67 billion by the end of the forecast period. This rapid growth is primarily propelled by the adoption of artificial intelligence (AI) and machine learning (ML) across various industry verticals, along with the escalating need for high-quality, diverse datasets that do not compromise sensitive information.

One of the primary growth factors fueling the synthetic data generation engine market is the heightened focus on data privacy and regulatory compliance. With stringent regulations such as GDPR, CCPA, and HIPAA being enforced globally, organizations are increasingly seeking solutions that enable them to generate and utilize data without exposing real customer information. Synthetic data generation engines provide a powerful means to create realistic, anonymized datasets that retain the statistical properties of original data, thus supporting robust analytics and model development while ensuring compliance with data protection laws. This capability is especially critical for sectors like healthcare, banking, and government, where data sensitivity is paramount.

Another significant driver is the surging adoption of AI and ML models across industries, which require vast volumes of diverse and representative data for training and validation. Traditional data collection methods often fall short due to limitations in data availability, quality, or privacy concerns. Synthetic data generation engines address these challenges by enabling the creation of customized datasets tailored for specific use cases, including rare-event modeling, edge-case scenario testing, and data augmentation. This not only accelerates innovation but also reduces the time and cost associated with data acquisition and labeling, making it a strategic asset for organizations seeking to maintain a competitive edge in AI-driven markets.

Moreover, the increasing integration of synthetic data generation engines into enterprise IT ecosystems is being catalyzed by advancements in cloud computing and scalable software architectures. Cloud-based deployment models are making these solutions more accessible and cost-effective for organizations of all sizes, from startups to large enterprises. The flexibility to generate, store, and manage synthetic datasets in the cloud enhances collaboration, speeds up development cycles, and supports global operations. As a result, cloud adoption is expected to further accelerate market growth, particularly among businesses undergoing digital transformation and seeking to leverage synthetic data for innovation and compliance.

Regionally, North America currently dominates the synthetic data generation engine market, accounting for the largest revenue share in 2024, followed closely by Europe and the Asia Pacific. North America's leadership is attributed to the presence of major technology providers, robust regulatory frameworks, and a high level of AI adoption across industries. Europe is experiencing rapid growth due to strong data privacy regulations and a thriving technology ecosystem, while Asia Pacific is emerging as a lucrative market, driven by digitalization initiatives and increasing investments in AI and analytics. The regional outlook suggests that market expansion will be broad-based, with significant opportunities for vendors and stakeholders across all major geographies.

Component Analysis

The component segment of the synthetic data generation engine market is bifurcated into software and services, each playing a vital role in the overall ecosystem. Software solutions form the backbone of this market, providing the core algorithms and platforms that enable the generation, management, and deployment of synthetic datasets. These platforms are continually evolving, integrating advanced techniques such as generative adversarial networks (GANs), variational autoencoders, and other deep learning models to produce highly realistic and diverse synthetic data. The software segment is anticipated to maintain its dominance throughout the forecast period, as organizations increasingly invest in proprietary and commercial tools to address their un

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