Data Anonymization, Privacy, and Business Security

Data Anonymization, Privacy, and Business Security

As personal data has grown into a valuable commodity for businesses and consumers alike, many people are concerned about the ways in which their information can be protected from unauthorized access and disclosure. To this point, data anonymization is one method that can be used to safeguard personal information, as the technique works to alter data so that the data cannot be used to identify a particular person. More specifically, Google defines the process as “a data processing technique that removes or modifies personally identifiable information; it results in anonymized data that cannot be associated with any one individual. It’s also a critical component of Google’s commitment to privacy.”

To illustrate the applicability of data anonymization on a large scale, the U.S. Census Bureau has used differential privacy to help protect the demographic data of American citizens for many years now. Subsequently, differentially privacy is a form of data anonymization that “enables researchers and database analysts to avail a facility in obtaining the useful information from the databases, containing people’s personal information, without divulging the personal identification about individuals.” To this end, there are many other techniques that can be used to anonymize personal data.

Data masking

One of the primary techniques that can be used to anonymize personal data is the process of data masking. Data masking creates a fake yet seemingly realistic version of a business or organization’s data by hiding legitimate data behind the veil of altered values for such information. For example, businesses looking to protect their personal information through the utilization of data masking could use symbols and characters such as asterisks or letters such as y and x as placeholders for the legitimate numbers and letters that may be present within the actual data set. Alternatively, a business could also randomize certain forms of personal data, such as account names and numbers, in an effort to thwart cybercriminals.


On the other hand, pseudonymization is another technique that can be used to anonymize personal information. Pseudonymization works to replace personally identifiable information with fictional information that “maintains referential integrity and statistical accuracy, thereby enabling business processes, development and testing systems, training programs, and analysis to operate normally.” Moreover, pseudonymization is ideal for businesses that need to protect their personal data while simultaneously using such information for other purposes, such as training, data warehousing, and testing products and services, among other things. A common example of pseudonymization is replacing a legitimate name with a placeholder name such as John Doe.

Synthetic data

Conversely, a third method that an organization can use to anonymize personal information is the creation of synthetic data. Much like data masking and pseudonymization, a business can use synthetic data to combat hackers and bad actors that may be looking to steal their personal information. However, the use of synthetic data to protect legitimate business information differs from these other techniques in that the data in question is created artificially through the use of an algorithm that automatically generates data. To do this, an algorithm will be trained to generate fake data after being trained on a database that contains an organization’s actual personal information.

Drawbacks of data anonymization

In spite of the many advantages of data anonymization as it relates to the protection of personal information, no data anonymization method is completely foolproof. This being said, many security researchers, in addition to cyber criminals that attack the databases of businesses and organizations, have proven that anonymized data can effectively be deanonymized. As stated by prominent data-driven marketing services company TechTarget, “de-anonymization is a data mining strategy in which anonymous data is cross-referenced with other data sources to re-identify the anonymous data source.”

For this reason, while data anonymization can undoubtedly be a useful tool in certain situations, businesses that are looking for a foolproof means to safeguard their data must look to more permanent methods of obfuscating such data. From data removal methods such as redaction to data conversion techniques such as encryption, a business looking to give their customers or clients complete assurance that their personal data will remain confidential at all times will invariably have to prevent others from accessing such data altogether.

As 2021 saw the most data breaches that had ever occurred in a calendar year throughout recorded history, businesses around the world are continuing to look for new ways to go about securing their personal information. Due to this fact, many businesses have begun using the process of data anonymization to accomplish these goals, as various data anonymization methods have been supported by privacy legislation around the world, including the EU’s landmark General Data Protection Regulation (GDPR). As a result, while data anonymization is already a common practice for many businesses, it will surely become even more commonplace in the near future.

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