Data anonymization

What is data anonymization? 

Data anonymization is a data protection process that hides sensitive and identifiable information with random characters before sharing the data for analysis or research purposes. The main goal of data anonymization is identity protection, so no one can link the anonymized data to the respective individual directly or indirectly. 

Data masking and data anonymization might appear similar, but have different techniques, purposes, and characteristics. Both are for data protection. Data masking is to hide characteristics while maintaining original format of the datasets, so they could be used in non-production environments. Unlike data masking, data anonymization is an irreversible process, mainly for sharing secure datasets with identities to the public. 

Data anonymization has usecases across industries, specifically useful for cases like healthcare research, public data sharing, marketing analytics, and more. 

Let's consider healthcare customer records as an example of data anonymization. It contains the customer's name, patient ID, Address, and diseases, which must be shared with a third party for further research. The anonymized dataset will not have any identifiers like address or names. Fields like DOB and diseases will be generalized to year of birth, as it’s required. Patient ID and patient names will be replaced with pseudonyms/unique codes.   

Anonymized data example for healthcare 

Data anonymization techniques 

Common data anonymization methods are data masking, pseudonymization, generalization, suppression, swapping, randomization, and tokenization. 

Data masking is a data anonymization technique that masks sensitive characters with unidentifiable yet realistic characters. Example: 123-456-7890 to XXX-XXX-XXXX 

Pseudonymization is a data anonymization method that assigns reversible pseudonyms to protect sensitive data. Pseudonymized data is often stored separately. Example: John Smith -> Customer 25. 

Generalization technique reduces data precision, turning specific values into a broad range of values. Example: Salary = $20,432. After generalization, $20k to $22k. 

Suppression is the removal of columns with sensitive data altogether. 

Aggregation is a technique that combines multiple data fields, so it cannot be linked to a specific identity. Example: sharing the average age, rather than the age of the individual. 

Swapping or shuffling is a data protection technique that swaps two protectable columns. Shuffling is mainly used for tables with categorical data to prevent linking attacks. 

Tokenization replaces sensitive characters with a reversible, non-sensitive token. Using a tokenization system, you can generate anonymization tokens and reverse them to see original data. 

There are many other data anonymization techniques too, like synthetic data generation, perturbation (Adding noise to values), k-anonymity, etc. 

Data anonymization tools 

There are countless data anonymization tools – both opensource and enterprise applications. Depending on your anonymization requirements, you could choose one from the following tools. 

Open-source anonymization tools like ARX, Amnesia, Anonimatron, Faker. Some (Anonimatron, Faker, etc) of these are github or R package, which you can download and install or integrate with your CI/CD pipelines. 

Commercial tools for anonymization include Talend data masking, Oracle data masking, Voltage SecureData, and IBM Infosphere Optim.  

Benefits of data anonymization 

1. Privacy protection: Ensures that your data is protected and safe while sharing in an open space. This prevents breaches, identity thefts, and many privacy violations. 

2. Data anonymization helps with GDPR and other compliance adherence. By ensuring protected data sharing, you safeguard customer information and avoid penalties and legal issues. 

3. Data anonymization is a must for a cross-collaborative experience, innovation, and other R&D activities for industries like healthcare, finance, SaaS, etc. 

4. Easy to train machine learning and AI models without risking the exposure of private data. This is specifically needed for building disease diagnosis models for healthcare industries which require training datasets of historical patient records. 

5. Anonymized data doesn’t carry any private information anymore and doesn’t require data minimization. It could be stored easily without an additional layer of security. There is also reduced impact of legal and financial risks, since the data is safe to use by all. 

6. in a world where data is becoming everything, data anonymization promotes ethical data usage among organizations, stakeholders, and third parties. 

While data anonymization has many benefits, it has challenges as well. One noteworthy challenge is how data anonymization leads to data loss. It's a task for data teams to establish the balance between data utility and privacy to prevent loss of data. Other challenges are that it requires complex process to select the right anonymization technique and the regulatory challenges where certain jurisdictions have different norms of what’s anonymized data.