How Does Data Anonymization Work?

How Does Data Anonymization Work?

Jan 14, 2022, 12:41:05 PM Tech and Science

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Data anonymization refers to the process of encrypting or erasing identifiers that link an individual to stored data in order to secure private or sensitive information. If you want to protect Personally Identifiable Information (PII), which includes names, social security numbers, and addresses, you can run it through this process that keeps the data but hides the source of the data.

Companies can use this process to comply with data privacy regulations meant to protect PII like contact information, health records, and financial details. 

This process is also referred to as data masking, data obfuscation, or data de-identification. De-anonymization, on the other hand, refers to data mining techniques that aim to re-identify encrypted or obscured data. Even if you remove identifiers from data, attackers can sometimes retrace the data anonymization process using de-anonymization techniques.  

Understanding Data Anonymization

Through their daily operations, businesses generate and store a massive amount of sensitive data. This data that's collected and exchanged across sectors and countries have played an important role in the technological advancements we all benefit from. For instance, through data from social media and e-commerce platforms, FinTech has revolutionized the way financial services are tailored to clients. 

However, in order to continue benefitting from this valuable data without jeopardizing confidentiality, companies need to implement strategies like anonymization. This way, if there's a breach, the data cybercriminals obtain will be useless. Because of the potential for abuse of sensitive information, data protection should be a top priority for any company. 

Regulators are cracking down on gross negligence when handling sensitive client information, which can cost businesses a lot of money through fines and loss of reputation. The PCI DSS (Payment Card Industry Data Security Standard) and other legal and regulatory standards impose significant fines on financial institutions. Other regulatory agencies have been established to supervise an organization's use or misuse of sensitive data.

What Kind of Data Needs to Be Anonymized

The answer to this question may seem obvious: sensitive data. But this leaves too much room for interpretation. What's considered sensitive data can vary between sectors. There are, however, a few categories of data that are considered sensitive across industries, and this is reflected in legislation

We'll start with a name – a key identifier in a data set. In case of a data breach, if cybercriminals can get access to the names in the database, they can easily connect it to other sensitive information they can profit from, so they need to be anonymized. 

Then we have credit card details. This category is pretty self-explanatory. You don't want a data breach resulting in your customers' credit card details getting stolen because not only will you get fined and lose trust and reputation, but you can also get sued by thousands or even millions of people. 

A phone number can be used to access other data sets that lead to a person's identity. 

Passwords also need to be anonymized, especially since so many people use the same password on multiple platforms. Cybercriminals know this, and whenever they manage to steal passwords from one platform, they will try them on others. 

This is not an exhaustive list. You also have photos, security questions, financial documents, product release documents, research and development data, and so on. Legislation is usually tailored to every industry, depending on the kind of data it collects and stores. 

Data Anonymization Techniques

Data anonymization can be achieved through multiple techniques and anonymization tools. Each technique is based on a separate set of technicalities and data types. They serve the same purpose but follow different underlying principles. Let's take a closer look at some of them. 

Data Masking 

This technique is based on hiding data by giving data sets another apparent identity. The protected data is mingled with other random, inauthentic sets of data. This makes it difficult for cybercriminals to de-anonymize the data. This technique can be performed in multiple ways:

  • Deterministic data masking – substituting a value in a column with another value across rows, tables, databases/schemas, and instances/servers/database types. For example, the first name David is always replaced with Mark. 
  • Dynamic data masking – Changing the data stream in such a way that the requester can no longer get access to the sensitive data without having to make changes to the original data. On-the-Fly Data Masking is related to Dynamic Data Masking, except On-the-Fly Data Masking is concerned with transferring data from one environment to another. Dynamic data masking happens on-demand at runtime. This technique doesn't require a second data environment to store the anonymized data. 
  • On-the-fly data masking - Data is transferred from one environment to another without touching the disk along the way. Companies that use continuous deployment or continuous delivery procedures don't have the time to make a backup and put it into the database's golden copy, making it necessary to send smaller subsets of masked data. 
  • Static data masking – Typically done on the golden copy of a database. This is a technique for modifying all sensitive datasets in a database. The data is then shared to a new location, with the original version being kept as a backup.

Data Swapping

Data swapping is as straightforward as the name suggests. It's a method of rearranging data sets by shuffling them around. There will be no resemblance between the original database and the resulting records. 

Data Perturbation

This approach anonymizes numerical data sets by rounding up the data sets based on a certain value and operation. For example, you may multiply all of the numerical numbers in your database by eight. You can also add, subtract or divide so that the data is altered. 

Pseudonymization

The EU's General Data Protection Regulation (GDPR) encourages the use of pseudonymization as a data management strategy. Identifiers or "pseudonyms" are used to substitute information that could reveal a person's identity. As a result, the data cannot be used to identify users.

The only method to link pseudonymized data to a specific user is to combine it with additional data stored and secured independently. This strategy allows businesses to collect and store the data they need to provide their products or services to customers while safeguarding their right to privacy.

Published by Rohan Manda

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