Data anonymization is a great way to protect sensitive data and privacy especially during various data process. As it offers a lot of benefits, you should try to learn more about this technique and implement it to your system.

Learn About Anonymity Right

Personal data is being used, collect, and store continuously which involved on many aspect of life which include healthcare data, work data, purchase data, ownership data, financial data and many other.

This is why a lot of countries began to form data privacy regulation such as CCPA in California and GDPR in Europe. With this regulation customers began to have a choice and also have anonymity right.

More About Data Anonymization

Actually data anonymization is the process to transform sensitive and personal data into a form that cannot be easily linked to a specific business entity or an individual. Thus it can reduce re-identification risk which needed to heighten data security and to comply with the data privacy regulations.

The process can help to protect various information that can be used to identify an entity or a person such as SSN, passport information, phone number, address as well as your name. This process also used during research where it manage large amount of data so privacy can be protected but their data can still be used.

However, you should remember that even with anonymization, it does not guarantee for compete anonymity especially when there is re-identification attack involved. The re-identification attack will combine sources that are publicly available with the previous anonymized data to then re-identify the entities or the individual.

This is why, cybersecurity team should know the limitation as well as the risk of the tools that they use to implement data anonymization especially when handling sensitive information and personal data.

Basic Data Anonymization Types

·     Data aggregation

In this method data which collected from various sources will be combined into single view data so it can be used by decision maker to get insights or to analyze the pattern or trend.

It can be done in various granularity level from the simplest summaries to a more complex calculation. It can also be implemented on various types of data such as text, numerical and categorical.

Then the aggregated data itself can then be presented using various forms which then be used on various purposes such as visualization, reporting, and analysis. The best thing is, this method can actually be implemented on data where other anonymization methods are already been implement before. This means it can be used to enhance the privacy and security of the data.

·     Data masking

In this method sensitive data will be replace with meaningless symbols, characters or digit into masked data. Then even if the data is masked it can still be used while also protecting the original sensitive data.

The masking can also be applied to the entire datasets or only specific field. It uses various technique to implement this method such as truncation, data shuffling, character substitution and many other.

·     Random data generating

In this method data will be shuffles randomly to obscure the sensitive information contained inside. It can also be applied to the entire dataset or only specific columns or field inside the database.

It is usually implement together with other masking or tokenization tools to enhance the privacy and security of the data. This method is very useful on clinical trials where the subjects must be randomly chosen and randomly assigned into various treatments group. And by implementing various data anonymization together then it can help to reduce bias while increasing the validity.

·     Data swapping

In this method value inside real data will be replace with fictitious value but with similar properties. For example, real name of an individual can be replaced with a fictitious name. This method actually very similar to the previous method however, the data is not shuffled. Instead it will be replaced using fictitious data.

·     Data generalization

In this method specific value inside the data will be replaced using generalized value to conceal sensitive information inside. It will replace areas, ranges or categories with specific value that already been determined before.

Common Technique Used To Implement Anonymization

·     K anonymity

With this technique you can ensure that information on the no 1 data can actually be distinguish among at minimum K-1 other data which stored inside the same set. So usually inside that set where will be at minimum K other record which have identical value that used to identify all of the attributes.

The goal of this technique is so hackers could not identify specific individual from inside the dataset only by looking at the identifying attributes value. This is because inside the dataset there are minimum K other individual inside which also have the same attributes value.

·     L diversity

With this technique, there is no data which able to be distinguished from minimum L other data inside the dataset only by looking at their sensitive attribute. This technique is created as an extension of the previous technique. The difference is this technique can be used to protect both sensitive and general attributes.

This technique still cannot fully guarantee the privacy of the data and the implementation is actually far more difficult. The reason is because it have to be able to both protect and identify sensitive attribute and this technique will only work when there are at minimum L attribute values that are distinct from each other inside the dataset.

·     T closeness

This technique combine both previous technique into one by making sure that distribution of sensitive attribute inside dataset will match the population target as close as possible.

This is why, it is also far more difficult to implement than both of the other technique. The reason is because it have to be able to protect and identify sensitive attribute and this technique only effective if the sensitive attributes distribution inside the dataset actually similar to the target population.

·     Randomized response

This technique can be implement inside a survey which will determine randomly if an answer to the question is honest or only pre-determine response is allowed in the form of Yes or No.

This will then allow the individual to give truthful answer on sensitive questions without the need to reveal their real response. It is work by putting a specific randomness level inside the process so the survey administrator will not know the real response of the individual.

However, since it use probabilistic concept then it cannot give comprehensive protection since there is still possibility for re-identification even though it is only remotely possible.

·     Differential privacy

This technique will put random noise inside the data so it will become unidentifiable. This framework usually used on visualization, reports, and data analysis which tries to balance privacy risk inside a dataset and the utility of the dataset.

To implement it, various randomization method are used for example sampling or perturbation. Then it use a specific parameter level to protect privacy called epsilon which able to control the number of noise put inside the original data. For smaller epsilon value then higher level of noise will be required.

It is important to remember than this technique can actually make the data becomes less accurate. Thus it is very important to balance the privacy protection with the utility. Furthermore, with the use of privacy parameter then there is still a small change of re-identification so it cannot guarantee complete security.

Data Anonymization Benefits

·     It will make identification of individual inside dataset becomes highly unlikely or even impossible to do.

·     It allows data sharing on legitimate purpose especially for research and analysis.

·     It make data privacy regulation compliance becomes much easier and quicker.

·     It will prevent hackers from getting sensitive data.

·     It help to minimize error risk such as incorrect link inside data.

·     It help to reduce cost since you can reuse the consent free information and there is no need to secure the data storage.

·     It will protect companies from loss of customer’s trust and market share.

·     It will safeguard the data against miss use as well as lowering the risk from insider exploitation.

·     It will help to increase the consistency and the governance of the result.

Disadvantages

·     It might reduce the data utility as it is removed or modify various personal elements which actually very important.

·     There is still re-identification possibility if the hackers are able to cross reference the data with other data.

·     It might need specialized tools and expertise to implement the method which will add on the complexity of the process and increase the cost.

·     It might not guarantee full protection on data privacy

·     It might not work if the data used is very sensitive and if the data used has very unique properties.

·     It might be resource intensive, time consuming also not scalable.

·     It might restrict your ability to get meaningful information while analyzing the data.

Conclusion

As you can see it is very important to protect personal information and sensitive data within various dataset. Thus, data anonymization is implement to achieve this goal. It can also help companies to comply with various data privacy regulations.