3 leading data masking methods and when to use them

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data masking

Now that data is the new oil, it’s a highly precious resource that needs to be safeguarded. Consumers are aware of the hunger for their data, and they want to protect it from both malicious use and unauthorized access. This creates immense — and well-deserved — pressure on apps, systems, and organizations to ensure data privacy.

While no one would argue against the importance of compliance with data privacy regulations, these requirements can create difficulties for business operations. Some business activities require teams to use or share data while still ensuring that no unauthorized entities can access it.

For example, software developers and testers might need real data to write effective code and check that everything is functioning correctly, but they could risk exposing the data in the process. Business analytics requires working with massive datasets, which can include sensitive information, to generate meaningful insights, but sharing such data with analysts or data scientists often violates data privacy regulations.

In other situations, organizations need to share data with third parties like vendors or outsourcing companies, while maintaining data privacy and confidentiality.

When data needs to be both shared and protected, companies turn to data masking. In this article, we’ll discuss what data masking is, review the three preferred methods of data masking, and provide advice on when each technique is most relevant.

What is data masking?

Data masking, sometimes also called data pseudonymization, refers to obscuring or anonymizing specific elements of sensitive data. With data masking, sensitive information, such as personally identifiable information (PII), personal health information (PHI), and financial records is hidden from unauthorized entities, but legitimate users can still access the data they need for their tasks.

Data masking is important to minimize the risk of data breaches and maintain compliance with data protection regulations such as GDPR, CCPR, HIPAA, and PCI DSS. It reduces the potential for malicious actors to access data and use it for identity theft or fraud, while also helping to bolster user trust and the company’s reputation.

In data masking, the sensitive data fields are altered or replaced with realistic, but fictitious, data, to remove anything that could identify the data subject or link them with their data. A slew of different data masking methods exists, including encryption, data shuffling, tokenization, data substitution, data scrambling, and nulling out data. Each technique has its own merits and drawbacks, so different methods are suitable for different situations.

1.   Data substitution

As you can guess from the name, data substitution involves swapping made-up data values for sensitive data. With substitution, the original data is entirely replaced by new information, while preserving the overall structure and format of the dataset. This sets it apart from approaches like encryption, which transform the data into secure representations of the original.

Substitution is often used in the context of software development and testing, or for situations where businesses are seeking analytics insights. It’s a good fit for these circumstances, because it retains the format and characteristics of the original data, while still concealing the sensitive information.

For example, a developer might need to test app functionality by using data that resembles production data. Substitution allows them to run datasets that mirror the original format, to create realistic test environments without exposing sensitive data to unauthorized users or potential security threats.

You do need to be careful, however, that your data substitution techniques are sufficiently robust so that your substituted values don’t inadvertently reveal original data or compromise the integrity of the dataset.

2.   Data encryption

With encryption, sensitive data is transformed into unintelligible text using cryptographic algorithms and keys. Even if malicious actors intercept or steal the data, they won’t be able to decipher it without the appropriate decryption key. Encryption is often used to protect particularly sensitive information, like PHI, financial data, and PII.

Encryption delivers a high level of security during transmission, storage, and processing, but it’s also reversible, so it preserves the original data for access in appropriate situations.

This makes it a good choice for sharing data with authorized users, protecting databases, and transmitting data over networks. For example, developers can use encrypted data in production environments, to safeguard it while maintaining the integrity of the original data.

However, you do need to ensure that encryption keys are managed securely so that they aren’t lost or exposed, which could block access to legitimate users or result in a data breach. Encryption is also complex and can be time-consuming, which can affect system performance and latency, and degrade user experience and operational efficacy.

3.   Data shuffling

Data shuffling is similar to data substitution, but it randomly reorders elements of sensitive data to retain the original values instead of replacing them with fictitious ones. The original relationships and patterns within the datasets are obscured, but their statistical properties and overall structure are preserved.

It’s a useful technique for protecting sensitive information while still enabling it to be used for analysis or certain operations. However randomly reshuffling data elements can erase granular insights and valuable correlations within the dataset, which can affect any conclusions you draw from analysis.

You also need to pay attention to procedures for documenting and validating the shuffling process, to lower the risk of harming data quality and reproducibility.

Despite the drawbacks, data shuffling is often used to share anonymized datasets for research, analysis, or collaboration, while protecting individuals’ privacy and confidentiality. For example, companies in healthcare and biomedical research might use data shuffling to anonymize patient records or genomic data for large-scale analysis, or marketers could apply it to use consumer data for demographic analysis and trend monitoring without disclosing individual identity.

Data masking is a crucial business technique

Data masking is a vital tool for any organization that wants to be able to make use of the sensitive data they collect while still protecting it and complying with data privacy regulations. With so many different techniques, you can make data-driven business decisions, uncover innovative insights, and develop and test effective applications and programs while delivering a high level of data confidentiality and security.

Read next: 80% airline apps are spying on your device, experts warn

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