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LEAF Labs · Synthetic Data

Synthetic data for AI when real data is scarce, sensitive or costly.

Many AI projects stall on data: too few examples, too few rare events, or records too sensitive to move freely. LEAF designs, generates and evaluates synthetic datasets for a specific task, and measures how useful and how safe they are before they are used.

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What Synthetic Data Is

Generated data, measured against the source.

Synthetic data is produced by models or simulations that learn the structure of a real dataset or domain. Synthetic datasets can preserve relevant statistical and task-specific properties while reducing exposure of original records.

They are not automatically accurate or private. Generators can miss patterns, invent implausible combinations or reproduce rare records too closely. Privacy and utility should therefore be evaluated using appropriate metrics for each use case.

Use Cases

Where synthetic data helps

Scarce data

Augment small datasets when collecting more real examples is slow, expensive or impossible, and measure whether the additional data actually helps the model.

Rare events and edge cases

Represent conditions that are rarely observed, such as faults, anomalies or unusual operating states, so that models see them during training and testing.

Sensitive data

Reduce exposure of original records in development, analytics and testing, with a privacy-risk assessment of every dataset before it is used.

Class imbalance

Rebalance under-represented classes or groups, while checking that the generated examples do not introduce new artifacts or biases.

Software testing

Provide realistic test data for data pipelines, applications and integrations, without copying production records into test environments.

Collaboration

Share representative data with partners or research teams at lower exposure, subject to disclosure-risk evaluation and your own data protection assessment.

Data Types

The data we work with

Tabular data

Records with mixed numerical and categorical fields, where relationships and constraints between columns have to be preserved.

Time series and sensor data

Signals from machines, sensors and IoT devices, where temporal patterns, correlations between channels and rare events matter.

Text

Documents, messages and domain-specific language for training and testing language models and NLP pipelines.

Images

Visual data for computer vision, including variations and conditions that are difficult or costly to capture.

Techniques range from statistical and probabilistic models to generative adversarial networks, variational autoencoders, diffusion models, transformer-based models and simulation. We choose them based on the data type, the size of the source dataset and the privacy constraints, and prefer simpler methods when they are sufficient.

The Process

From source data to a validated dataset

01

Assess

We study the source data, the intended task and the privacy requirements, and agree on what utility and acceptable risk mean for this use case.

02

Generate

We select and train the generation approach. Where formal privacy guarantees are required, differential privacy can be incorporated.

03

Evaluate

We apply synthetic-data quality metrics for statistical similarity and task-specific utility, and run a disclosure-risk evaluation, with domain experts reviewing plausibility.

04

Deliver

We deliver the dataset or the generation pipeline with documentation and a quality report, and integrate it into your training or testing workflow.

Quality and Privacy

The questions every synthetic dataset must answer

Statistical similarity

Does the synthetic data reproduce the distributions, correlations, constraints and temporal patterns that matter in the source?

Task-specific utility

Does it work for the intended task? A common test is to train on synthetic data and evaluate on held-out real data.

Privacy risk

How much could it reveal about the original records? We assess disclosure risk with methods such as distance to the closest record and membership inference testing.

These properties trade off against each other: stronger privacy protection usually costs some fidelity. Synthetic data alone does not guarantee privacy, so the balance is chosen and documented for each use case.

Generation pipelines can be deployed in architectures designed to support specific regulatory, security and data-residency requirements, including on your own infrastructure.

Where It Fits

Part of LEAF Labs

Synthetic data is a Labs capability: we prototype, engineer and validate data pipelines that feed real models. It often starts from an Advisory assessment and connects to other Labs work on edge AI and connected devices.

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AI on the Edge

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AI Automation

FAQ

Questions & Answers

Common questions about synthetic data at LEAF.

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Synthetic data is artificially generated data designed to reproduce relevant statistical patterns and structure of a real dataset or domain, without being a copy of the original records. It can take the form of tabular records, time series, text or images.

It depends on the task. Synthetic datasets can preserve relevant statistical and task-specific properties, but they approximate the source rather than replace it. LEAF measures utility for the intended use, for example by training a model on synthetic data and testing it on held-out real data, and reports where the synthetic data falls short.

No. Synthetic data can reduce exposure of original records, but generative models can memorize and reproduce rare or unique records. Privacy has to be evaluated with disclosure-risk metrics for each dataset. Where formal privacy guarantees are required, differential privacy can be incorporated, usually with a trade-off in utility.

Not automatically. Whether a synthetic dataset can be treated as anonymous depends on the re-identification risk in the specific context, and generating it from personal data is itself a form of processing. LEAF provides privacy-risk evaluations that support your data protection assessment; the legal determination remains with you and your advisors.

Tabular records, time series and sensor data, text and images. Each type requires different generation techniques and evaluation metrics, so the approach is chosen for each dataset.

Statistical and probabilistic models, generative adversarial networks, variational autoencoders, diffusion models, transformer-based models and simulation. The choice depends on the data type, the size of the source dataset, the required fidelity and the privacy constraints. Simpler methods are preferred when they are sufficient.

Along three axes: statistical similarity (distributions, correlations and temporal patterns), task-specific utility (performance on the intended task) and privacy risk (disclosure-risk evaluation, such as distance to the closest original record and membership inference testing). Results are documented in a quality report delivered with the dataset.

Yes. Generation pipelines can be deployed in architectures designed to support specific regulatory, security and data-residency requirements, including on your own infrastructure, so that source data does not need to leave your environment.

It can be used to augment rare classes or simulate conditions that are hard to observe, such as faults or anomalies. Synthetic edge cases need validation with domain experts, because a generator can only produce what its training data or simulation rules make plausible.

Describe your use case, your data and your constraints. LEAF assesses feasibility and proposes an approach, often starting with a pilot on a representative subset of the data.

Have a model that needs data you cannot easily collect or share?

Discuss your dataset