Data anonymization for training models

Introduction

Data anonymization for training models refers to the process of removing or modifying personally identifiable information before using datasets to train artificial intelligence (AI), machine learning (ML) and deep learning systems. Organisations collect large volumes of information from healthcare records, financial transactions, customer databases, scientific research and digital platforms to develop predictive and analytical models.

However, training datasets may contain sensitive information such as names, addresses, medical histories, genetic records, financial details and biometric information. If this information is exposed or misused, individuals may face privacy violations, discrimination and identity theft.

Data anonymization helps reduce these risks while allowing organisations to use information for research, innovation and commercial development. Nevertheless, anonymisation must be effective because AI models may sometimes memorise or reveal information contained in their training datasets.

Legal framework governing data anonymization

1. Digital Personal Data Protection Act, 2023

The DPDP Act is relevant when AI training involves digital personal data relating to identifiable individuals. It establishes requirements concerning lawful processing, security safeguards and personal data breaches, subject to applicable commencement provisions and rules.

Genuinely anonymised information that no longer relates to an identifiable individual is generally distinguished from personal data. However, merely removing names or replacing identifiers does not necessarily make a dataset anonymous.

2. Information Technology Act, 2000

The Information Technology Act, 2000 provides legal protection against unauthorised access, data theft and computer-related offences.

Important provisions include:

Section 43: Provides compensation for unauthorised access, copying or damage to computer systems and data.

Section 66: Addresses computer-related acts committed dishonestly or fraudulently.

Section 66C: Deals with identity theft involving passwords and other identification features.

Section 72: Addresses breach of confidentiality and privacy in specified circumstances.

Section 70B: Establishes CERT-In and its cybersecurity incident-response responsibilities.

These provisions may become relevant when training datasets are unlawfully accessed, copied or disclosed.

3. General Data Protection Regulation (GDPR)

The European Union's GDPR provides an important framework for anonymisation and AI model training.

Article 4(1): Defines personal data.

Article 4(5): Defines pseudonymisation.

Article 5: Establishes principles of lawful processing, purpose limitation and data minimisation.

Article 6: Provides requirements for lawful processing.

Article 9: Provides additional protection for special categories of personal data.

Article 25: Establishes data protection by design and by default.

Article 32: Requires appropriate technical and organisational security measures.

Under Recital 26, information that has been genuinely anonymised so that an individual is no longer identifiable is outside the GDPR's personal data scope.

4. Copyright and intellectual property laws

Training datasets may contain copyrighted books, photographs, research publications, software and other protected materials. Therefore, anonymisation does not automatically provide permission to use copyrighted material for model training.

The Copyright Act, 1957 and applicable international intellectual property laws must also be considered.

Major challenges in data anonymization for model training

1. Re-identification risks

Removing names and identification numbers does not guarantee anonymity. Individuals may be identified by combining datasets with publicly available information.

2. Model memorisation

Machine learning models may memorise particular training examples, especially when datasets contain sensitive or unusual information. In some circumstances, information may be extracted from trained models.

3. Inference attacks

Attackers may analyse a model's outputs to infer whether particular individuals were included in its training dataset or to obtain information about them.

4. Data linkage

Information from different sources may be combined to identify individuals even when each dataset appears anonymised separately.

5. Loss of research accuracy

Excessive anonymisation may remove important information needed for accurate model training. Organisations must balance privacy protection with data quality.

6. Third-party data sharing

AI developers frequently obtain training information from external vendors, research institutions and cloud providers. Unclear data-sharing arrangements may create legal and privacy risks.

Important anonymization techniques

Organisations can use several technical methods to reduce privacy risks.

Data masking: Replaces sensitive values with modified or hidden values.

Pseudonymisation: Replaces direct identifiers with artificial identifiers while keeping the additional identifying information separately.

K-anonymity: Ensures that individuals cannot be distinguished from at least a specified number of records based on selected identifying attributes.

Differential privacy: Adds carefully controlled statistical noise to reduce the ability to identify individual contributions.

Federated learning: Allows models to be trained across decentralised datasets without necessarily transferring the underlying raw data to a central location.

Synthetic data generation: Creates artificial datasets that reproduce useful statistical patterns without directly reproducing original personal records.

These methods reduce privacy risks but do not automatically guarantee complete anonymity.

Important case laws

1. Breyer v. Germany (2016)

The Court of Justice of the European Union considered whether dynamic IP addresses could constitute personal data when additional information held by another party could enable identification. The judgment established that information may remain personal data even when identification requires additional information. This principle is important for AI training because removing direct identifiers does not necessarily establish effective anonymisation.

2. Google Spain SL v. AEPD (2014)

The Court of Justice of the European Union examined the processing of personal information by search engines and recognised circumstances in which individuals could request removal of certain search results. The judgment is relevant to the broader principle of responsible personal data processing by digital platforms.

3. Justice K.S. Puttaswamy v. Union of India (2017)

The Supreme Court of India recognised privacy as a fundamental right under Article 21 of the Constitution. The judgment established that informational privacy deserves legal protection. It is relevant to AI model training because organisations must consider individuals' privacy when collecting and analysing personal information.

4. Lloyd v. Google LLC (2021)

The United Kingdom Supreme Court examined a representative claim involving alleged unlawful collection of browser-generated information. It rejected the proposed representative action because the claimant had not established that every affected individual had suffered the same damage merely from the alleged loss of control over personal data. The decision is relevant to understanding privacy litigation and the need to establish legally recognised harm.

5. Vidal-Hall v. Google Inc. (2015)

The English Court of Appeal considered claims involving misuse of private information and data protection arising from online tracking. The judgment recognised that compensation for distress could be available under the relevant data protection framework. It is relevant to the legal consequences of improper personal data collection and processing for digital services.

These cases do not directly determine the legality of anonymisation techniques used in AI training. They provide analogous principles concerning identifiability, privacy rights and the consequences of improper data processing.

Compliance obligations for organisations

Organisations developing AI and machine learning models should establish a structured data governance programme.

Conduct privacy impact assessments before collecting training datasets.

Identify and remove unnecessary personal identifiers.

Test datasets for re-identification risks before sharing them.

Use differential privacy or other suitable privacy-enhancing technologies where appropriate.

Maintain records of data sources, permissions and processing purposes.

Review third-party data providers and contractual obligations.

Test trained models for memorisation and information leakage.

Establish procedures for responding to personal data breaches.

Maintain appropriate retention and deletion policies.

Review compliance with applicable domestic and international data protection laws.

Conclusion

Data anonymization for training models is an important part of responsible artificial intelligence and machine learning governance. India's DPDP Act, 2023, Information Technology Act, 2000 and the European GDPR provide important legal principles concerning personal data protection and security. Judicial decisions such as Breyer, Puttaswamy and Lloyd v. Google demonstrate the importance of identifiability, informational privacy and lawful data processing. Effective anonymisation requires appropriate technical safeguards, re-identification testing, transparent data governance and continuous monitoring to support innovation without compromising individual privacy.