How can you protect against re-identification attacks on anonymized data?

1 What are re-identification attacks?

Re-identification attacks are methods of de-anonymizing data by using additional information, such as external databases, metadata, or statistical analysis, to infer the identities of the data subjects. Re-identification attacks can be motivated by various reasons, such as financial gain, malicious intent, or research curiosity. Re-identification attacks can be classified into three types: linkage attacks, inference attacks, and reconstruction attacks.

2 How to prevent linkage attacks?

Linkage attacks are the most common type of re-identification attacks. They involve matching anonymized data with other data sources that contain identifying information, such as names, addresses, or phone numbers. To prevent linkage attacks, you should use strong anonymization techniques, such as k-anonymity, l-diversity, or t-closeness, which ensure that each anonymized record is indistinguishable from at least k other records, and that each group of records has sufficient diversity and closeness of sensitive values. You should also avoid using unique or quasi-identifiers, such as social security numbers or zip codes, or replace them with random or synthetic values.

3 How to prevent inference attacks?

Inference attacks are based on using statistical or machine learning methods to deduce sensitive information from anonymized data, such as gender, age, or health status. To prevent inference attacks, you should use noise injection, which adds random or controlled errors to the data to reduce its accuracy and utility for attackers. You should also use differential privacy, which guarantees that the presence or absence of an individual in the data does not affect the outcome of any analysis. You should also limit the amount and granularity of data that you release or share, and use access control and encryption mechanisms.

4 How to prevent reconstruction attacks?

Reconstruction attacks are the most advanced type of re-identification attacks. They involve using multiple anonymized datasets or queries to reconstruct the original data or a close approximation of it. To prevent reconstruction attacks, you should use secure multiparty computation, which allows multiple parties to perform computations on their data without revealing it to each other or to a third party. You should also use homomorphic encryption, which allows performing operations on encrypted data without decrypting it. You should also use federated learning, which enables distributed learning from local data without centralizing it.

5 How to monitor and audit your data anonymization and pseudonymization processes?

To protect against re-identification attacks, you should not only apply robust anonymization and pseudonymization techniques, but also monitor and audit your data anonymization and pseudonymization processes regularly. You should conduct risk assessments and privacy impact assessments to identify and mitigate potential vulnerabilities and threats. You should also test and evaluate the effectiveness and performance of your techniques, and update them as needed. You should also comply with relevant privacy laws and regulations, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA), and follow ethical and responsible data practices.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?