Best data anonymization tools and techniques

There is a popular ongoing debate about the underlying brain of Artificial Intelligence (AI). The development of algorithms and existing machines that are capable of thinking like people comes with the need to balance technical knowledge with moral objectives.

As a result, while Artificial Intelligence (AI) opperations consistently develop, personal data protection in this field has become a matter of the utmost importance. Common ethical aspects, in both the private and public sectors, such as privacy, responsibility and data security are now in the spotlight.

According to a February 2021 report about the International Information Security Community issued by the ISMS Forum Data Privacy Institute (DPI), more than 78% of companies' Data Protection delegates reviewed their privacy model in light of the sky-high fines recently imposed on them.

Thus, anonymized data is no longer only a challenge for public entities, but for any company that is keen on complying with the General Data Protection Regulation (GDPR) and using its data responsibly.

 

What is data anonymization?

Anonymization technology was developed in order to deal with the growing volume of sensitive data that organizations use and store. Modern anonymization techniques are a branch of Natural Language Processing (NLP) that operate with rules and dictionaries to fine-tune detecting any term that can be considered personal data.

Therefore, anonymization generates non-identifiable datasets that can be used and disclosed without the legislative need for additional consent, given that they are no longer considered personal information.

By striping the data of its personal identifying traits, companies can perform data analytics and "big data" with the assurance that if there is an information leak or if the corporation is hacked, the data will not contain any type of compromising information in terms of privacy and confidentiality.

 

The emergence of modern tools in data anonymization

The emergence of data anonymization tools that protect individuals' and corporations' private activity allows for the credibility of the data collected, manipulated and exchanged to be preserved.

The limitations of traditional de-identification methods are becoming more evident, creating room for modern Privacy-Enhancing Technologies (PET) that produce effective results with structured and unstructured data in a vast range of fields and sectors.

Although there are many techniques involved in data anonymization, which we will explain below, they are mainly all based on the classification of name entities and other auxiliary techniques known as masking (of social security numbers, phone numbers, email addresses, credit cards, etc.)



Related content: Data Protection and Anonymization in the Context of Financial and Legal Services



Popular data anonymization and pseudonymization techniques

Data pseudonymization and anonymization techniques in all their forms seek to reduce the identifiability of data that belongs to a person from the given original dataset, and break it down to a level that does not exceed a pre-established risk threshold.

 

1. The difference between pseudonymization and anonymization

Pseudonymization is a data de-identification tool that substitutes private identifiers with false identifiers or pseudonyms, such as swapping the identifier "AB" with the identifier "CD". This maintains statistical precision and data confidentiality, allowing changed data to be used for creation, training, testing, and analysis.

It is not considered a strict form of anonymization since, with this method, personal data's linkage to the identity of the individual is only reduced. However, it is not anonymous data, so data protection regulations might apply.

Therefore, pseudonymization prevents the identification chain from breaking. Meaning that, even if the data is dissociated, re-identification is possible. The main advantage of this technique is that the document can be read once generated and the private information is no longer traceable.

 

2. Data masking

Also known as character masking, it refers to the disclosure of data with modified values. Data anonymization is performed by creating a mirror image of a database and implementing alteration strategies, such as character shuffling, encryption, or term or character substitution. For example, a value character may be replaced by a symbol such as "." or "x."

This technique makes identification or reverse-engineering very difficult, so it is typically used for billing scenarios, such as the masking of credit card information (the account number or the CVV, for instance.)

 

3. Data swapping

Often known as permutation and shuffling, this technique rearranges dataset attribute values so that they remain present but do not correspond with their original records. Switching attributes (columns) that include recognizable values, such as a date of birth, can make a huge impact on anonymization while respecting original information.

This method is easily reversible and only effective if there is no need to evaluate data based on relationships between the information contained within each record. 

 

4. Synthetic data

Unlike other data anonymization techniques, synthetic datasets consist of complex imitation versions of actual data rather than modified data. Synthetic datasets have many similarities to the actual data, such as format and relationships between data attributes.

Synthetic data is algorithmically generated information with no relation to any actual case. The data is used to construct artificial datasets based on statistical methods instead of modifying or using the original dataset and compromising privacy and protection.

 

5. Data substitution

As the name suggests, this tool allows users to replace the content of a database column with random values from a predefined list of fake, but similar-looking data, so that the information cannot be traced back to a recognizable individual. 

This technique has the advantage of keeping the integrity of the original information intact. However, to successfully leverage this method, users must have lists with the same volume, or more data than the amount trying to be anonymized.

 

6. Data blurring

Data blurring works in a very similar way to generalization, by reducing the precision of the disclosed data to minimize the possibility of identification. As the term suggests, blurring uses an approximation of data values instead of original identifiers, making it hard to identify individuals with certainty.

This is often achieved through the use of ranges (by not giving specific values) and by eliminating hard facts from the documents.

 

7. Data encryption

The data encryption technique translates personal data into an entirely different form or code. This way, sensitive information is replaced with data in an unreadable format. Authorized users may have access to a confidential key or a password that allows them to retrieve the data in its original form.  

It is largely used for information stored in the cloud, allowing you to secure remote locations, outsourcing and licensing. It also stops service providers from accessing or inadvertently exposing your data.



Keep reading: When to review a translation? The importance of human translation

 

Why should you anonymize your data?

There is a broad spectrum of advantages associated with anonymizing data, regardless of the industry sector in which your business operates. 

From medical research to medical enhancements, software development and business performance, anonymized data is the only way forward in the near future, providing some key advantages to companies throughout the world:

  • Protecting businesses against the potential loss of trust and therefore market share due to data misuse and exploitation risks.
  • Fueling digital transformation by providing protected data to be used in generating new market value.
  • Increasing data governance and preserving privacy from outsiders, while acting as a barrier to external influence.
  • Complying with regulatory laws, including the GDPR, and ensuring an ethical data manipulation and transfer.

 

Pangeanic: your partner in data anonymization

While there is no universal way to deal with anonymization, mixed techniques based on neural models and customizable anonymization profiles are always the best solutions for any particular organization.

Given the wide range of techniques that are currently available, we strongly recommend seeking a balance between the degree of risk involved in re-identification or exposure of confidential information and the purpose for which the data is being used. 

At Pangeanic we work with a combination of anonymization and pseudonymization methods to provide you with a made-to-measure solution adapted to your individual needs. Do you want to know which one is the best for your business? Let’s talk!

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