Synthetic, Anonymized, and Pseudonymized Data in Indian Healthcare: A Privacy-Centric Perspective under the DPDP Act

India's healthcare sector is undergoing rapid digital transformation, driven by innovations in telemedicine, electronic health records (EHR), and AI-based diagnostics. With the increasing use of digital tools comes an urgent need to protect patients' personal data. The Digital Personal Data Protection (DPDP) Act, 2023, establishes a framework for managing and processing personal data responsibly. Within this context, understanding the differences between synthetic data, anonymized data, and pseudonymized data becomes essential.

This article explores these data types from a healthcare lens and outlines their significance, especially under the DPDP Act, 2023, and global data protection frameworks like GDPR and HIPAA.

What is Synthetic Data in the Context of Data Privacy and DPDP Act?

Synthetic data is artificially generated data that mirrors the structure and statistical properties of real-world data but does not correspond to any actual patient or individual. It is created using advanced techniques such as AI-driven generative models, simulations, or statistical methods. Because it does not relate to a real person, synthetic data is generally outside the scope of the DPDP Act.

In healthcare, synthetic data allows organizations to simulate patient profiles, test digital health applications, and train AI models—all without exposing actual patient information.

Anonymized vs Pseudonymized vs Synthetic Data: Key Differences

Use Cases in Indian Healthcare

a. Synthetic Data

• A healthtech startup generates synthetic ABHA-linked patient profiles to test their digital health platform’s integration with ABDM.

• A hospital creates synthetic diabetic patient datasets to train an AI-based prediction model for retinopathy.

b. Anonymized Data

• A government hospital anonymizes its cancer registry data before publishing a state-wide oncology report.

• Researchers working on COVID-19 trends anonymize data to analyze symptoms, comorbidities, and mortality rates.

c. Pseudonymized Data

• A diagnostic lab shares test reports internally using tokenized patient IDs to ensure internal workflows function without revealing patient names.

• An insurance company processes pre-authorization requests using pseudonymized policyholder data.

Regulatory Perspectives: India and the World

DPDP Act (India)

• Synthetic Data: Not considered personal data. No compliance obligations.

• Anonymized Data: Also excluded if truly anonymized and irreversible.

• Pseudonymized Data: Falls under the definition of personal data. Subject to consent, purpose limitation, and data protection obligations.

GDPR (EU)

• Similar to India’s DPDP: synthetic and anonymized data are not considered personal data. Pseudonymized data is regulated.

HIPAA (USA)

• HIPAA defines de-identified data (similar to anonymized) and permits its use without patient consent. Pseudonymized data (with a code or key) still falls under PHI (Protected Health Information).

Singapore’s PDPA and China’s PIPL also adopt similar distinctions between anonymized, pseudonymized, and synthetic data, with pseudonymized data still being regulated.

Benefits and Risks in Healthcare

Best Practices for Indian Healthcare Providers

• Use synthetic data for software testing, AI training, or sandbox environments.

• Ensure true anonymization before sharing data for research or publication.

• Apply pseudonymization when operational needs require reversibility—ensure key management and access controls.

• Maintain documentation to demonstrate compliance, especially for pseudonymized data.

• Train staff on the implications of each type of data and relevant safeguards.

In the Indian healthcare sector, where digitization is accelerating, safeguarding personal data is both a legal and ethical obligation. Understanding the distinctions between synthetic, anonymized, and pseudonymized data helps organizations navigate compliance under the DPDP Act while enabling innovation. Each type has its place—when used wisely, they ensure that patient privacy is preserved without stifling digital health progress.