top of page
Search

The Role of Synthetic Data Use in Data Privacy-Preserving AI Models

Updated: May 28


How Can Synthetic Data Save Data Privacy-Preserving AI Models?
How Can Synthetic Data Save Data Privacy-Preserving AI Models?

Introduction

Artificial intelligence (AI) has become an indispensable tool in data analysis, automation,

and organizational decision-making. However, as AI systems become more pervasive,

concerns over data privacy and data security continue to escalate. Organizations that

process sensitive personal data, like healthcare providers, financial institutions, and

social media platforms, face increasing data privacy and data protection legal and

regulatory scrutiny regarding how they manage and protect user information. Organizations

will need to mitigate the data privacy risks while enabling AI adoption and AI innovation.

As a result, many organizations view synthetic data as a viable solution.


Key Terms and Definitions

To better understand the role of synthetic data in data privacy-preserving AI models, it is essential to define several key terms:

  • Privacy-Enhancing AI Models: AI systems designed with built-in data privacy measures, such as differential privacy, federated learning, and homomorphic encryption, to minimize risks associated with personal data exposure while maintaining utility.

  • Differential Privacy: A mathematical framework that ensures AI models generate outputs without revealing specific details about any individual in the dataset.

  • Federated Learning: A decentralized machine learning approach that allows AI models to be trained across multiple devices or locations without exchanging raw data.

  • Homomorphic Encryption: A cryptographic technique that enables computations on encrypted data without decrypting it, ensuring data confidentiality during processing.

  • Data Anonymization: The process of removing or obfuscating personally identifiable information to prevent the re-identification of individuals within a dataset.

  • Re-Identification Attacks: Attempts to link anonymized data back to individuals by leveraging auxiliary information or data linkages.

  • Generative Adversarial Networks (GANs): A class of machine learning models used to generate realistic synthetic data by pitting two neural networks, a generator and a discriminator, against each other.


Understanding Synthetic Data

Synthetic data is artificially generated information that mimics the statistical properties of

real-world datasets without containing any actual personally identifiable information (PII).

Unlike traditional anonymization techniques, which can still be vulnerable to re-

Identification attacks, synthetic data provides an additional layer of data security by

ensuring that no direct one-to-one mapping exists between real and synthetic records.

There are two primary methods for generating synthetic data:

  • Rule-Based Generation – Uses predefined rules and heuristics to create data that resembles real-world patterns.

  • AI-Generated Data – Leverages deep learning models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) to produce highly realistic synthetic datasets.


Real Personal Data Usage. Synthetic Data Usage

Real personal data usage provides high fidelity and authenticity but comes with significant

data privacy risks, legal and regulatory challenges, and potential biases. Conversely, synthetic data offers a data privacy-preserving alternative that is cost-effective, adaptable, and capable of reducing bias; however, its effectiveness depends on the quality of thedata generation models. Organizations must weigh these factors based on their specific AI model needs, the legal and regulatory requirements, and their risk tolerances.


Synthetic Data and Privacy-Preserving AI Models Examples

Synthetic Data:

  • Healthcare: Hospitals and research institutions use synthetic patient records to train AI models for disease prediction and diagnosis while ensuring compliance with privacy regulations like the Health Insurance Portability and Accountability Act of 1996, as amended.

  • Finance: Banks and financial institutions generate synthetic transaction data to detect fraudulent activity without exposing real customer information.

  • Retail: E-commerce platforms use synthetic customer behavior data to improve recommendation algorithms while safeguarding user privacy.


Data Privacy-Preserving AI Models:

  • Google’s Federated Learning: Google employs federated learning in its Gboard keyboard app to improve predictive text suggestions without transmitting users’ personal data to centralized servers.

  • Apple’s Differential Privacy Implementation: Apple integrates differential privacy techniques into iOS and macOS to collect user behavior insights while minimizing the risk of individual data exposure.

  • IBM’s Homomorphic Encryption for Secure AI Computation: IBM Research has developed AI systems that leverage homomorphic encryption to enable privacy-preserving data analysis on encrypted datasets without compromising confidentiality.


Comparing Real Data and Synthetic Data in Data Privacy-Preserving AI Models: When

implementing AI-driven solutions, organizations often must decide between using actual

personal data or synthetic data. The following table presents a comparative analysis of the

two approaches.

Feature

Real Data

Synthetic Data

Privacy Risks

High risk of exposure to PII and re-identification

Lower risk due to no direct PII inclusion

Regulatory Compliance

Subject to EU GDPR, CCPA, PIPL, and other data privacy and data protection laws and regulations

Easier compliance as data is artificially generated

Bias in AI Models

Can contain societal and systemic biases

Can be generated to mitigate existing biases

Data Availability

Requires consent, complex sharing agreements

Can be generated as needed for specific use cases

Cost and Time

Expensive and time-consuming to collect and manage

Lower costs and faster generation

Model Accuracy

High fidelity but subject to missing or inconsistent data

Depends on the quality of generation models

Use Case Flexibility

Limited by availability and ethical concerns

Adaptable for multiple applications


Data Privacy Benefits of Synthetic Data in Data Privacy-Preserving AI Models

  • Enhanced Data Anonymization: Traditional anonymization techniques, such as masking or tokenization, can be susceptible to re-identification attacks. In contrast, synthetic data ensures privacy by removing any direct link to real-world identities, making it an effective tool for organizations dealing with sensitive user data.

  • Compliance with Data Protection Regulations: Data privacy and data protection legal and regulatory frameworks such as the European Union’s General Data Protection Regulation (EU GDPR), the California Consumer Privacy Act (CCPA) as amended by the California Consumer Privacy Act, and China’s Personal Information Protection Law (PIPL) impose strict rules on personal data handling. Since synthetic data does not contain real PII, organizations can use it to train AI models without violating these data privacy and data protection laws and regulations. Organizations should review each law or regulation to ensure compliance before using synthetic data in their data privacy-preserving AI models.

  • Secure Data Sharing: Many organizations collaborate with external vendors, researchers, or partners who require access to data for AI model development. Sharing raw datasets introduces risks of data breaches and unauthorized access. With synthetic data, companies can provide high-fidelity alternatives that retain the same analytical value while protecting sensitive information.

  • Bias Reduction and Fairness in AI: AI models trained on real-world data often inherit existing biases present in society. By curating synthetic datasets with balanced representations, developers can mitigate bias in AI models and promote fairness in decision-making systems.


Risks Associated with Using Synthetic Data in Data Privacy-Preserving AI Models

While synthetic data offers significant advantages for use in data privacy-preserving AI

models, it is not without risks. Organizations must carefully evaluate these risks to ensure

that synthetic data effectively serves its intended purpose. The key risks include:

  • Residual Privacy Risks (Re-Identification): Poorly generated synthetic data may still contain patterns or structures that allow for re-identification of real individuals, particularly if the synthetic dataset closely mirrors the original data. Adversaries with access to both synthetic and real data might perform linkage attacks to infer sensitive information.

  • Bias and Data Representativeness: Synthetic data derived from biased original datasets can perpetuate these biases, leading to unfair or inaccurate outcomes in AI models. The challenges in capturing the full diversity and complexity of real-world data may result in models that do not generalize well in practical applications.

  • Regulatory and Compliance Challenges: The legal status of synthetic data under frameworks like the EU GDPR remains uncertain, as some data privacy and data protection laws and regulations do not explicitly address its use, creating potential compliance ambiguities. Notably, the EU AI Act and similar AI laws aim to address this issue. Organizations may need to demonstrate that their synthetic data meets AI and data protection standards, particularly when used for decision-making that impacts individuals.

  • Data Utility vs. Data Privacy Trade-Off: Balancing data privacy and data utility is a complex task; over-sanitizing synthetic data to enhance privacy can diminish its usefulness, rendering AI models less effective. Conversely, overly realistic synthetic data may inadvertently pose privacy risks.

  • Model Security and Adversarial Risks: Synthetic data generation models can be susceptible to adversarial attacks, where inputs are manipulated to produce misleading or biased synthetic datasets. AI models trained on synthetic data may be vulnerable to adversarial attacks if the dataset fails to represent real-world variations accurately.

  • Lack of Standardization and Validation Methods: The absence of universal standards for evaluating the quality, privacy preservation, or fairness of synthetic data makes it challenging for organizations to assess their reliability. Without proper validation techniques, synthetic data may introduce hidden errors or anomalies that negatively impact AI performance.

  • Dependence on High-Quality Source Data: The effectiveness of synthetic data hinges on the quality of the real-world dataset used for its generation. Incomplete, biased, or low-quality source data will result in flawed synthetic data. Poor-quality input data can lead to misleading AI outcomes.

  • Intellectual Property and Ownership Issues: Legal and ethical concerns may arise regarding the ownership of synthetic data, particularly when it is derived from proprietary datasets. Organizations must navigate licensing agreements, data-sharing policies, and intellectual property rights to ensure that the use of synthetic data aligns with bothbusiness and legal objectives.

  • Mitigating These Risks: To address these challenges, organizations should consider:

    • Implementing rigorous privacy-enhancing techniques (e.g., differential privacy, k-anonymity) when generating synthetic data.

    • Validating the statistical fidelity of synthetic data to ensure it accurately represents real-world patterns without compromising privacy.

    • Adopting bias detection and fairness assessments to prevent synthetic data from reinforcing discrimination.

    • Establishing AI and data privacy governance frameworks to ensure the use of synthetic data aligns with ethical, regulatory, and business objectives.

    • Continuously test and refine AI models trained on synthetic data to detect potential vulnerabilities.

  • Final Thoughts: While synthetic data is a powerful tool for preserving data privacy and AI models, it is not a panacea. Organizations must approach their adoption of AI strategically by balancing AI governance, data privacy, data utility, and data security to realize their potential fully.


Key Questions for Businesses Considering Using Synthetic Data in Data Privacy-Preserving AI Models

  • How well does synthetic data preserve the statistical integrity and utility of real-world data?

  • Does synthetic data align with AI and data privacy legal and regulatory compliance requirements across different jurisdictions?

  • How can synthetic data help mitigate biases and improve fairness in AI models?


Conclusion

By embracing synthetic data, organizations can revolutionize their AI strategies, transforming data privacy challenges into opportunities for broader adoption and innovation. Synthetic data’s use in data privacy-preserving AI models reduces some risks associated with handling real PII. However, organizations must also address the risks associated with using synthetic data in privacy-preserving AI models.


Using synthetic data in privacy-preserving AI models ensures compliance with stringent data privacy laws and regulations, mitigates bias, and enhances model performance through the use of scalable, diverse, and high-quality datasets. As AI continues to drive the future of industries—from healthcare and finance to retail and beyond—organizations that adopt synthetic data will gain a competitive edge, unlocking new possibilities without the constraints of ethics, law, or regulation. The future of AI belongs to those who prioritize both AI innovation and data privacy. Will your organization lead the way, or be left behind?


Sources

 
 
 

Comments

bottom of page