Anonymization vs Synthetic Data. How to Safely Generate Training Data Without Personal Information?

Anonymization of visual materials is currently a key process for many organizations processing personal data. When companies and public institutions collect photos or video recordings containing images of people, license plates, or other personal data, they must ensure compliance with GDPR regulations. An especially interesting application of anonymization is the possibility of using anonymized materials to create synthetic training datasets for AI systems.

Synthetic data generated from anonymized materials provide not only a privacy-safe solution, but also open new possibilities for the development of artificial intelligence systems without incurring legal risks. In this article, I will analyze how anonymization processes can be used to generate valuable training datasets free from all Personally Identifiable Information (PII).

What Is Visual Data Anonymization and How Does It Affect Synthetic Data Generation?

Visual data anonymization is the process of removing or modifying elements of photos and videos that could lead to the identification of individuals. The most common techniques include face blurring, masking license plates, and removing other personal identifiers. Unlike pseudonymization, properly conducted anonymization ensures that data can no longer be linked to a specific person.

Anonymized visual materials can serve as a base for creating synthetic data. Synthetic data are artificially generated datasets that preserve the statistical properties of the originals but do not contain any actual information about specific individuals. Machine learning algorithms can be trained on such data without risking privacy breaches.

This process is especially important for organizations working with sensitive data, which must comply with strict personal data protection regulations while seeking to develop AI-based technologies.

What Are the Legal Grounds for Using Synthetic Data in the Context of GDPR?

The General Data Protection Regulation (GDPR) sets strict requirements for processing personal data. According to Article 4 of the GDPR, personal data means any information relating to an identified or identifiable natural person. Synthetic data, when properly generated from anonymized source materials, are not subject to GDPR regulation because they do not relate to specific individuals.

The European Data Protection Board (EDPB) has issued anonymization guidelines, emphasizing that for data to be considered anonymized, the process must be irreversible. This means that even the data controller should not be able to re-identify individuals from anonymized data, even with additional information.

The use of synthetic training data is therefore a legally compliant solution for organizations wishing to develop AI systems without violating the privacy of individuals whose data they process.

How to Effectively Anonymize Visual Materials Before Generating Synthetic Data?

Effective anonymization of visual materials requires the use of appropriate techniques and tools. The first step is to identify all elements that could lead to person recognition - faces, license plates, distinctive markings, and environmental features.

Modern anonymization solutions, such as Gallio Pro, use advanced AI algorithms to automatically detect and blur faces and license plates. On-premise software provides an additional layer of security, as sensitive data never leaves the organization’s infrastructure.

An important aspect is the depth of anonymization - the degree of blurring or masking should be tailored to the intended use of the data. For synthetic data, it is crucial that anonymization is irreversible while still preserving features useful for algorithm training.

Can AI Algorithms Be Used to Automate Anonymization Before Creating Synthetic Data?

The use of artificial intelligence in visual material anonymization significantly increases the efficiency and accuracy of the process. Modern AI solutions can detect faces, license plates, and other personal identifiers with high precision, even in poor lighting or partially obscured situations.

Deep learning algorithms can be trained to recognize an ever-widening range of potential personal identifiers. What’s more, automation greatly accelerates the preparation of large datasets for processing and synthetic data generation.

Nonetheless, human oversight remains necessary, especially in edge cases or with sensitive materials. A hybrid approach combining automation with expert data protection verification ensures the highest level of security.

What Are the Benefits of Using Synthetic Data Compared to Anonymized Real Data?

Synthetic data offers several major advantages over anonymized real data. Most importantly, synthetic datasets can be generated in unlimited quantities with precisely specified parameters, allowing for perfectly balanced AI training sets.

Another benefit is the ability to simulate rare or hard-to-capture scenarios. For example, in city surveillance systems, it is possible to generate synthetic data depicting dangerous situations that rarely occur but are crucial for the training of safety systems.

Synthetic data also resolves issues related to seasonality or geographic limitations of data availability. They can be generated to represent different seasons, lighting conditions, or locations, greatly increasing the versatility of trained systems.

From a legal perspective, working with synthetic data minimizes the risk of violating personal data protection regulations because this data has never represented real individuals.

What Technical Challenges Are Associated with Generating Synthetic Data from Anonymized Materials?

Creating high-quality synthetic data from anonymized materials poses several technical challenges for organizations. The first is maintaining representativeness - synthetic data must faithfully reflect the statistical properties of original datasets despite removing identifying information.

Another challenge is computational efficiency. Generating advanced synthetic data, especially for video materials, requires significant computing power and specialized software. On-premise solutions must be scalable to meet these demands.

Quality verification of generated data is also crucial. Mechanisms are needed to assess whether synthetic data preserve essential features for the intended use while ensuring no elements remain that could enable re-identification.

How to Ensure That Synthetic Data Generation Complies with GDPR Requirements?

To ensure GDPR compliance, a comprehensive approach to data protection must be adopted throughout the synthetic data generation process. Above all, source materials must be properly anonymized before being used to generate synthetic data. Anonymization should be performed irreversibly, in accordance with EDPB guidelines.

Conducting a Data Protection Impact Assessment (DPIA) before implementing a synthetic data generation system is recommended, especially if the process is part of a larger personal data project. DPIA helps to identify potential risks and plan mitigation measures.

Documentation of the entire process - from sourcing data, through anonymization, to generating synthetic data - is a key element of GDPR accountability. Regularly checking whether generated data genuinely prevents person identification is necessary.

Case Study: How Can Police Use Synthetic Data Generated from Anonymized Video Materials?

Police units routinely collect large amounts of video from body cameras, city surveillance, or intervention footage. Using these materials for AI system training is problematic due to privacy concerns and the sensitive nature of many recorded situations.

In one implementation, a regional police headquarters used video anonymization software to automatically blur faces and vehicle license plates. The anonymized materials then served as a base to generate synthetic data that retained characteristics crucial for training risk detection systems but contained no personal data.

Synthetic data were used to train algorithms for detecting potential threats in public spaces, increasing the effectiveness of preventive actions. Importantly, such materials could also be safely shared with other police units and used in training resources without risking privacy breaches.

This case demonstrates how anonymized data can be transformed into valuable training datasets while respecting legal requirements regarding personal data protection.

How to Verify the Quality of Synthetic Data for AI Training Use?

Quality verification of synthetic data is a key step before using them to train AI systems. The first step is statistical analysis comparing feature distributions in synthetic and original (anonymized) datasets. Good quality synthetic data should preserve key patterns and correlations.

The next step is testing the performance of machine learning models trained on synthetic versus real data (if available). Performance differences can flag issues in synthetic data quality.

An expert review is also recommended, where domain specialists can identify potentially unrealistic elements in generated data. For visual materials, this could include image inconsistencies, unnatural object positions, or background generation errors.

Regular monitoring and iterative improvement of synthetic data generation enhances their utility for AI training over time.

What On-Premise Software Works Best for Anonymization Before Synthetic Data Generation?

Choosing the right on-premise software for visual material anonymization is crucial to the security of the entire process. Solutions such as Gallio Pro offer advanced automatic anonymization of faces and license plates using artificial intelligence algorithms, providing a solid foundation for subsequent synthetic data generation.

Key features for anonymization software before synthetic data generation include:

• High accuracy in detecting elements requiring anonymization
• Configurable degree and methods of anonymization (blurring, pixelation, masking)
• Efficiency in processing large data volumes
• Automation of entire anonymization process for datasets
• Full control over data processed within the organization’s infrastructure

On-premise software ensures that sensitive data never leaves the organization’s infrastructure, which is crucial for institutions that handle highly confidential materials, such as law enforcement or medical units.

It is recommended to conduct tests on representative sample materials before selecting a specific solution, to assess anonymization effectiveness in the context of organizational requirements. Check out Gallio Pro and see how our solution can streamline the anonymization process before generating synthetic data.

How Can Synthetic Data Help Safely Share Visual Materials with Media and Partners?

Sharing visual materials with the media, research partners, or publishing to social platforms poses a major challenge for personal data protection. Synthetic data offer an elegant solution, allowing valuable information transfer without risking privacy violations.

Instead of releasing anonymized real materials, organizations can generate synthetic datasets that illustrate the same phenomena, trends, or events but do not include images of actual people. This approach is especially valuable for law enforcement, which frequently needs to communicate with the public by showing intervention or preventive action footage.

Synthetic data can also be used to create training materials that can be safely distributed to different units without concern for data protection regulations. This is crucial for international cooperation, where legal requirements for personal data processing may differ by jurisdiction.

The Future of Synthetic Data Amid Increasing Privacy Demands

As public awareness and stricter regulations regarding personal data processing continue to grow, the significance of synthetic data will steadily rise. Organizations will seek ways to develop AI systems without the legal risks associated with using real personal data.

Technologies for generating synthetic data will evolve towards ever-greater fidelity to the original while preserving total anonymity. Development of specialized solutions for various sectors, taking into account their specific needs and legal requirements, can be expected.

One promising direction is synthetic data creation in the federated learning paradigm, where models are trained locally on real data, and only model parameters or generated synthetic data are shared - thus eliminating the need to centralize sensitive information.

For organizations processing visual materials, investing in anonymization and synthetic data generation technology will become not only a legal requirement but also a competitive advantage, enabling innovation while respecting privacy.

FAQ - Frequently Asked Questions About Synthetic Data from Anonymized Materials

Are synthetic data generated from anonymized materials subject to GDPR?

No, provided the anonymization process was conducted properly and irreversibly. Synthetic data do not relate to specific individuals and thus are not personal data under GDPR.

How can you ensure that synthetic data does not enable re-identification of individuals?

Advanced anonymization methods should be applied before generating synthetic data, and re-identification testing should be conducted. It's also recommended to consult the process with data protection experts.

Can synthetic data completely replace real data for AI system training?

In many cases, yes - especially where general patterns and dependencies are key. There are, however, applications demanding exceptional precision, where real data may still be necessary, albeit strictly protected.

What are the costs of implementing a synthetic data generation system from anonymized materials?

Costs include anonymization software (e.g. Gallio Pro), adequate IT infrastructure, and staff training. However, this investment pays off by minimizing legal risk and enabling broader data use.

Are there industries for which synthetic data are especially valuable?

Yes, synthetic data are particularly valuable for sectors processing large volumes of sensitive personal data, such as healthcare, public safety, finance, or insurance. They enable innovation while complying with strict privacy regulations.

How to convince decision makers in an organization to invest in synthetic data technology?

Highlight the business benefits: reduced legal risk, broader data usability, innovation potential, and competitive advantage. A pilot project demonstrating value can also help gain buy-in.

Can small organizations also use synthetic data?

Yes, anonymization and synthetic data generation solutions are also available for smaller organizations. Download the Gallio Pro demo and discover how our solution can be tailored to different organizational needs.