Data processors handle an abundance of data — including personal information about individuals. As the collection and use of data become more widespread, governments continue to enact laws that protect personally identifiable information (PII). Failing to comply with such laws means risking serious fines and penalties, and damaging public trust. Masking PII through pseudonymization is one way to protect it.
Here's a breakdown of PII and how pseudonymization effectively conceals sensitive information.
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Table of Contents:
- What is Personally Identifiable Information?
- Why do Data Processors Need to Think About PII?
- What is PII Pseudonymization?
- How does Pseudonymization Improve PII Security?
- Six Ways to Achieve PII Pseudonymization
- How Xplenty Can Help
What is Personally Identifiable Information?
Each law dealing with personal information offers a unique definition of what constitutes PII. Therefore, it is critical to review the specific wording of all laws that apply to your organization to make sure you're in compliance. However, there's a basic starting point for most PII: It is information that could identify a specific individual, either by itself or when combined with other data;
An additional layer is a distinction between sensitive and non-sensitive PII. Nonsensitive information may already exist in the public square, but it does not cause harm to an individual. The disclosure of sensitive PII, on the other hand, could cause harm. The line between sensitive and non-sensitive PII is not always clear, and it is safest for data processors to assume they should always protect both.
Common examples of sensitive PII include:
- Full name
- Social security number
- Driver's license number
- Passport information
- Financial information, including credit card numbers
- Medical information, including personal health records
By contrast, the following are usually nonsensitive examples of PII:
- Date of birth
- Place of birth
Non-sensitive PII alone cannot identify a specific individual. However, it can link to other pieces of data that may lead to personal identification.
PII pseudonymization is an effective technique to shield both types of PII from accidental or deliberate exposure.
Why do Data Processors Need to Think About PII?
It's clear that personally, identifiable information exists — but why is it such a big concern for data processors? Unlike the data repositories of previous generations, today's data stores are bigger and more comprehensive. Digital businesses commonly rely on data to do business. It is essential to create data analytics that drives marketing, sales, operations, and almost every aspect of modern commerce.
There are three major reasons data processors have the added responsibility of protecting the PII among this huge data volume and why they commonly use PII pseudonymization to do it:
Legal mandate: Privacy laws at the federal and state level, such as California's CCPA, impose an obligation of PII protection on businesses. Laws in non-US jurisdictions, like the European Union's GDPR and India's Personal Data Protection Bill, also commonly apply to data processors not based in these geographic areas if they collect the PII of people in those countries.
Competitive advantage: Companies collect PII for a reason. In the aggregate, it can become important data analytics that increases profitability. Not protecting PII creates a risk of that information falling into competitor's hands.
Public trust: Data breaches make consumers wary of sharing private information. If a company cannot avoid data disclosures, the public should know that their PII remains encrypted and therefore indecipherable to hackers.
PII pseudonymization does not prevent all forms of a security breach. But it does ensure that if a hacker reaches the location of PII, they cannot comprehend, recognize, read or use the obscured data.
What is PII Pseudonymization?
PII pseudonymization involves changing data so the personal information is no longer directly recognizable in the data set. It is still possible to reveal personal information by reversing pseudonymization. All elements of the data set still exist, but in different places. In that way, pseudonymization is distinct from anonymization. In the latter, the process deletes the original PII. With pseudonymization, you can always get the info back.
The choice between anonymization or pseudonymization depends on the organization. Some businesses, like marketers or e-commerce sites, can use pseudonymized information to achieve business purposes. In some cases, they may take the leap to anonymization without losing their business advantage. However, other organizations, like healthcare providers, need regular access to real personally identifiable information and cannot anonymize their data. Therefore, pseudonymization may be their only option.
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How does Pseudonymization Improve PII Security?
Most data networks employ several levels of security, including:
Physical security: such as making sure hardware components and technology equipment are safe from damage or theft.
Network security: such as imposing firewalls around the periphery of the network so hackers are less likely to reach internal data stores.
Encryption: such as masking internal data by scrambling or another process so, even if breached, the data is unusable.
Pseudonymization only helps accomplish one of these three levels: encryption. It is still possible for hackers to steal personal information. But if it's pseudonymized, they can't use it for nefarious purposes like identity theft.
This process does more than protect. It helps enterprises fully exploit their data assets. Laws such as the GDPR give businesses license to process PII within greater limits after pseudonymization. Once they have taken this step to conceal PII, they can use the data for more purposes than if it were in its original state. Therefore, there's an additional incentive for businesses to comply with this regulation, beyond the simple avoidance of fines.
Six Ways to Achieve PII Pseudonymization
There are at least six ways you can pseudonymize your data. These methods refer to how the original data changes in order to confuse or discourage hackers.
Data masking: dummy characters replace sensitive information. An example is replacing all but the last four digits of a social security number (SSN) with a capital "X."
Data scrambling: the characters, digits, or letters in a recognizable data set are re-arranged. With this type of pseudonymization, "Smith-Reynolds" might become "Hitmsrye-Donls."
Data encryption: the transfer of the data into an encrypted format, which someone can only decode if they have the right key.
Data tokenization: tokens, or random strings of letters or characters, replace the sensitive information. Tokens are traceable back to the original source information.
Data blurring: deliberate errors merge with the sensitive data. As an example, "Jane Smith-Reynolds, date of birth 06/07/1981" might become "Jane Smith-Reynolds, date of birth 07/07/1980."
Data bucketing: general information replaces more precise information. With this process, a specific age might become an age range. For example, 47-years-old become "40s."
Each of these methods involves transforming the original data through a defined process. To achieve this, you must extract raw data from a source, transform it, and then load it into a target warehouse. Pseudonymization happens at the "transform" stage, according to the parameters you set.
How Xplenty Can Help
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A data security strategy must include not just PII pseudonymization but also other techniques to achieve robust protection. Because of the importance of data security, it is vital that everyone in your organization can achieve it — and that's what Xplenty offers. It's an intuitive, user-friendly platform, and you don't need to be a coder or data processor to use it.
Xplenty is an ETL solution that offers high-security protocols. Its specifications comply with data protection laws, letting you achieve your transformation objectives while adhering to the law. To learn more, contact us today to arrange a 14-day demo of the platform.