Assess generated synthetic data on accuracy, privacy, and speed
QA reports assess how accurate and reliable synthetic data is in order to meet privacy standards for confident decision-making.
Reliable and accurate synthetic data is a critical feature for synthetic data solutions. Our platform is aligned with industry standards, which provide robust benchmarks, models, and metrics.
Evaluating the quality of synthetic data involves measuring how accurately the generated data retains the statistical properties of the original dataset. This assessment shows that the synthetic data reflects the same patterns, distributions, and correlations as the real data.
Privacy protection metrics measure the protection of the generated synthetic data in terms of privacy, offering a clear assessment of how well sensitive information is protected in the generated data.
When synthetic data is shared externally, a privacy evaluation is required to verify that privacy metrics meet defined thresholds. These thresholds help reduce re-identification risks to an acceptable minimum.
Synthetic data utility metrics
Distributions
Distributions illustrate the frequency of variables within given categories or values and are accurately captured by the Syntho Engine.
Correlations
Correlations show the relationship between variables, illustrating the degree to which variables are related. The Syntho Engine accurately captures these relationships.
Multivariates
Multivariate distributions and multivariate correlations take us beyond singular dimensions, providing a comprehensive view of how multiple variables are related. The Syntho Engine captures these relations.
Example industry-standard metrics for evaluating privacy and fairness
Disclosure
Demonstration that there is no risk of disclosing sensitive information about specific, sensitive columns in your dataset.
Considers information disclosure
Overfitting protection
Demonstration by measuring the distance between the real and synthetic data, that your synthetic data doesn’t too closely match the real data.
Considers overfitting
Fairness
Demonstration that the synthetic data improves fairness when it comes to predicting value. Equalized odds particularly looks at the true positive rate (TPR) and false positive rate (FPR) of any predictions you’re trying to make.
Considers fairness
The QA Report is offered in a separate module so it will be:– Always up to date– Adapt to evolving quality standards– Only applied when relevant, since not all datasets or use cases require the same level of quality assurance.
Explore other features that we provide
Test Data Management
De-Identification & Synthetization
Comprehensive Testing with Representative Date.
Rule-Based Synthetic Data
Simulate Real-World Scenarios.
Subsetting
Create Manageable Date Subsets.
Smart De-Identification
PII Scanner
Identify PII automatically with our AI-powered PII Scanner.
Synthetic Mock Data
Substitute sensitive PII, PHI, and other identifiers.
Consistent Mapping
Preserve referential integrity in an entire relational data ecosystem.
AI Generated Synthetic Data
Quality Assurance Report
Assess generated synthetic data on accuracy, privacy, and speed.
Time Series Synthetic Data
Synthesize time-series data accurately with Syntho.
Upsampling
Increase the number of data samples in a dataset.
Data utility refers to how well a dataset meets the needs of its intended use. It encompasses accuracy, completeness, consistency, reliability, and relevance. High-quality data is accurate and free from errors, inconsistencies, or duplications, demonstrating that it can be effectively used for analysis, decision-making, and operational purposes.
Synthetic data quality pertains to how closely synthetic datasets mimic real-world data’s statistical properties and characteristics. It evaluates the fidelity of the generated data, including its accuracy, reliability, and relevance, demonstrating that synthetic data is a valid substitute for actual data in various applications.
It is a synthetic data quality evaluation displayed in quality assurance and demonstrates the accuracy, privacy, and speed of the synthetic data compared to the original data. It provides a detailed analysis of the synthetic dataset, including metrics for accuracy, privacy, and performance, indicating that the data meets high standards.
At Syntho, we understand the importance of reliable and accurate synthetic data. That’s why we provide a comprehensive quality assurance report for every synthetic data run. Our quality report includes various metrics such as distributions, correlations, multivariate distributions, privacy metrics, and more. This way, you can easily assess that the synthetic data we provide is of the highest quality and can be used with the same level of accuracy and reliability as your original data.
Our quality assurance report evaluates:
Synthetic data privacy metrics are crucial because they asses if generated data does not reveal sensitive or personally identifiable information.
High-quality synthetic data offers several benefits:
Unlock data access, accelerate development, and enhance data privacy.
Keep up to date with synthetic data news
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