Table of Contents
What types of anonymization methods exist?
Data anonymization is a broad term that can cover multiple methodologies. Some of the most common ones are:
- Data Masking: In data masking, you change different values within the original information. You may also hide certain aspects if you need to. By doing this, the original PII can no longer be identified – but at the same time, you can use the data for your needs.
- Pseudonymization: Rather than masking information, you will replace certain areas with pseudonyms in many cases. You might also choose to utilize artificial information identifiers that are separate from your PII.
- Generalization: This makes information less specific than PII tends to be, which is important for protection. For example, you might change locations so that a user is no longer identifiable based on their geography. Dates may also be changed, as could other aspects like age and gender.
While these are the three most common ones at the moment, other possibilities are being researched. Federated learning is one of these, though its adoption still isn’t as widespread. While it’s not currently widely available, synthetic data could potentially become a more common option in the future.
Are there any alternatives to the traditional anonymization strategies for big data?
As technology evolves alternative data anonymization strategies are being explored; however, these aren’t always very widespread. Besides synthetic data generation, homomorphic encryption is also a potential data anonymization alternative.
When choosing any anonymization tactic, you must understand the benefits and drawbacks. For example, while you may be able to improve the protection of people who were previously identifiable with PII, you may still need to consider ethical implications and the like.
It’s worth researching each data anonymization method and alternative before embarking on your next project. You should also note that you may need to try different methods in varying scenarios.
Does anonymization provide true anonymity?
Although data anonymization aims for anonymity, achieving complete anonymity is often complex and depends on various factors. It can be a useful way to protect people, but you need to consider your role in making it that way. For example, what you do to make data anonymous will play a significant role.
The level of knowledge and sophistication that potential hackers have is another possible influencer, so it’s a good idea to consider this in advance. Moreover, you need to think about the data itself and its level of quality. Understanding all of these is imperative for keeping user data as safe as possible and maximizing anonymization.
It’s also a good idea to not rely just on data minimization; instead, you should build a full security stack if you wish to achieve optimal results. For example, you must implement data access control points. Moreover, you should encrypt all information regardless of its sensitivity.
What are the benefits and drawbacks of anonymizing data?
When you implement a data anonymization strategy, it’s important to think about the pros and cons of doing so. Let’s now look at these.
Pros:
- Privacy: It’s a good idea to anonymize PII to lower the threat of criminals from causing harm to customers. Anonymization should highlight ethical data use when implemented correctly; it’s up to the company to show that it prioritizes this.
- Compliance: Data protection is important under multiple regulations, including HIPAA and GDPR. Anonymization is therefore a key consideration when aiming to comply with these laws.
- Data Sharing/Analysis: Use data anonymization to share information within your company without the risks of doing so with PII. Doing so is important for allowing all teams to reach their goals, and you should therefore prioritize it.
- Data Value Preservation: You should anonymize data with privacy in mind, but you’ll still need it to achieve your goals. So, you may want to look at data anonymization as a middle ground between compliance, ethics, and results.
Cons:
- Data Accuracy: Be careful not to distort your anonymized information too much; you need accurate data for your insights, and this should be at the forefront of your mind regardless of the anonymization method that you implement.
- Applicability: You need to consider whether anonymization is applicable to the data you’re trying to implement it with. For example, highly sensitive data may need more comprehensive tactics.
Conclusion
Data anonymization is a key consideration for any business that needs data to perform key functions but doesn’t want to compromise privacy. Understanding the different types available is important, whether they’re traditional or non-traditional methods.
It’s also essential that you know what data is more compatible with anonymization practices. You may need to use something more robust in some circumstances, and identifying when this is the case is something you should do in advance.
Before using data anonymization, make sure that you also understand the pros and cons.
