Safeguarding Privacy with Data Anonymization

Data today is digital fuel for any organization. As organizations are heavily using data for driving their decisions, it has become very important to safeguard this valuable asset. When it comes to a large variety and volume of data, it may carry personal and sensitive information. If this information gets leaked, it can cause a huge loss to the business. This makes data anonymization no longer optional.

In this blog, we’ll cover what data anonymization is, reasons to implement it, types of data that should be anonymized, common anonymization strategies, and the challenges. 

What is Data Anonymization?

Data anonymization is the process by which personal data is transformed using various masking techniques such that it cannot be identified directly or by any external party. This is done to protect the confidential data of users and their privacy. Data anonymization necessarily does not block its usage, rather allows its usage for analysis, reporting, and machine learning while preserving the privacy of data.

This is done by making data fall outside the data protection laws and guidelines defined by GDPR, CCPA, and HIPAA, which makes it secure to use for analytics and other development purposes.

Why is Data Anonymization Needed?

Now that we have understood data anonymization, let’s discuss why it is needed. 

1. Regulatory Compliance

Many countries have data protection regulations like GDPR that mandate anonymized handling of PII data. Missing these guidelines can cost companies penalties and reputation damage. Anonymization helps to ensure that compliance is met with no or minimal exposure of sensitive data.

2. Data Sharing and Collaboration

Organizations may need to share their data with partners, vendors, or internal departments for analysis purposes. Data anonymization helps to safeguard this data in transit or at rest.

3. Internal Testing and Development

Often, when software is tested, it may need test systems to feed real production data to simulate the environment. Anonymizing this data can help dev teams to work on developments while taking care of data privacy.

4. Security Breach Mitigation

If data is anonymized at rest, even if it gets breached, the risk of exposing PII data is comparatively low compared to not doing it at all.

What Kind of Data Should Be Anonymized?

Though anonymization can be done on all kinds of datasets, it should be particularly done on the following data types.

Data Type	Examples
Personal Identifiable Information (PII)	Names, Social security numbers, Email addresses, Phone numbers
Health data	Medical history, Lab reports, Insurance information
Financial data	Credit card numbers, Account balances, Transaction history
Behavioral data	IP addresses, Clickstreams, Purchase patterns
Location data	GPS coordinates, Travel logs
Biometric data	Data that contains information on handprints or retina information
Racial data	Data that typically includes information about an individual’s race, ethnicity, or cultural background.
Political data	Data that tells a person’s political beliefs, opinions, affiliations, or activities.

Common Data Anonymization Techniques

Now that we have discussed data types on which anonymization should be done, in the section below, we discuss various techniques that can be used for anonymizing data. There’s no one-size-fits-all approach to it, but based on use case and risk profile, a combination of techniques can be used.

1. Data masking

In data masking, the original value of the data is replaced with fictional data. This is used mostly for development environments and UI testing. For example, replacing a real name with “John” to use the original user’s data in the dev system.

2. Pseudonymization

In this technique, original data is tokenized using various hashing techniques and is replaced with the original names. These tokens are also known as pseudonyms, hence the name pseudonymization. With this technique, the data is reversible under controlled conditions. For example, replacing user IDs with hashed values.

3. Data Shuffling 

As the name suggests, data is shuffled within a column randomly. This can help developers to keep the statistics intact while hiding identity. For eg, names in all rows can be swapped in a table.

4. Data Generalization

In this technique, the data is replaced with values within a range. This helps to reduce the uniqueness of data. For eg age of 16 can be replaced with “10-20” 

5. Differential Privacy

In this technique, noise is added to statistical data. This helps to make records non-reversible, and even if the data gets hacked, attackers will only find wrong information.

6. Suppression

In this technique, values that are highly sensitive and at high risk are completely removed from the dataset. For example, this technique can be used with medical data to hide rare diseases of patients.

Read about data integrity.

Key Challenges in Data Anonymization

Though anonymization is extremely important to protect one’s data, it also comes with certain challenges and limitations. Let’s discuss a few of them.

• Re-identification Risk: Anonymized data can sometimes get leaked through cross-referencing from other sources or de-hashing it due to weak strategies.
• Data Utility Loss: Applying anonymization to data can sometimes also make it useless. It is important to understand privacy and utility before implementing them.
• Complexity at Scale: It is not always possible to do manual anonymization, especially at a large scale. Organizations dealing with real-time or large-scale data pipelines need automated and rule-based systems to anonymize data.
• Changing Regulations: Privacy laws keep evolving. Thus, it is important to keep anonymization techniques updated to remain compliant with the regulations and save oneself from heavy losses and penalties.

Conclusion

As data becomes the central fuel to run businesses, privacy should be the first thought in any modern data architecture. Data anonymization is powerful to reduce any risk for your insightful data. This can help maintain regulatory obligations as per your country and use case.

As we discussed its challenges and identified key areas, it becomes important to do thoughtful implementation and monitoring and choose the right tools to maintain its effectiveness. Platforms like Hevo can help your organization to implement anonymization seamlessly across the data pipeline while maintaining scalability, security, and not compromising on speed. The right tools, like Hevo, can help you safeguard not only your data but also long-term trust in your brand.

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