Organizations in virtually all industries are embracing technologies that allow them to analyze massive data sets in order to gain market insights, optimize operations and boost profits. However, such big data initiatives raise concerns about data privacy. A variety of data anonymization techniques help address these concerns.
When organizations gather, store, process and share large data sets, there is always the risk of leaking customers’ personally identifiable information (PII). Data privacy laws such as the California Consumer Privacy Act (CCPA) and EU General Data Protection Regulation (GDPR) impose significant penalties on companies that expose PII.
Of course, consumer PII is a valuable target for cybercriminals, who can use it to create fraudulent personas and perpetrate many different types of crimes. According to one recent study, 97 percent of all data breaches involve the exposure of PII.
Data anonymization improves privacy by masking, modifying or removing identifiers that could connect stored data to a specific individual. This allows organizations to meet legal and regulatory requirements while retaining the ability to analyze and extract insights from their full data sets.
Following are some of the key techniques for anonymizing personal data:
Data masking. This technique replaces words or characters with random symbols. The database structure is maintained, but select characters are replaced with a “mask” character such as “*” or “x”. Typically, only a portion of a data value is masked. For example, a Social Security number might be rendered as ***-**-2345.
Pseudonymization. In this method, a personal identifier is replaced with a fake, but still unique, identifier. For example, the identifier “Robert Smith” might be changed to “Xislmv Lurvq.” The result is an identifier that can’t be linked to a real person but retains its integrity for data analysis purposes. The GDPR explicitly recommends pseudonymization as one way to reduce privacy risks.
Data scrambling. This technique is similar to pseudonymization, but instead of creating a totally fake identifier, an algorithm simply jumbles the characters. Using the above example to illustrate, “Robert Smith” might be changed to “Bretor Imths.”
Data swapping. Also known as shuffling or permutation, this is a technique for mixing up data attributes so they no longer correspond with original records. For example, a database table might have separate columns for names, job title and salary. Randomly shuffling each column preserves the original data values but makes it impossible to accurately align data with a personal identity.
Generalization. In this process, some data details are eliminated to make a record less specific. For example, numerical values might be stripped out of addresses, leaving only street names. Removing such detail improves privacy without affecting the basic accuracy of the data.
Synthetic data. This technique creates artificial data sets that imitate rather than modify actual data. An algorithm uses standard deviations, medians and other statistical measures to create a model based on patterns found in the original dataset.
Data anonymization techniques are important safeguards for data analytics projects because they help ensure that private information isn’t leaked while being shared among colleagues, departments or organizations. Anonymization isn’t foolproof, but it can make it far more difficult for criminals to get their hands on personal information. In many cases, organizations will use multiple anonymization techniques along with strong encryption to protect data and achieve compliance.
If you’re looking to extract value from your data, we invite you to give us a call. We can help you learn more about anonymization and other security tools and techniques for protecting PII.