In today’s digital-first economy, there is a humongous amount of personally identifiable consumer information strewn across the internet. There are multiple touchpoints where users are required to share their personal information. For instance, whether it is to play an online game, participate in webinars, attend virtual classes, or even shop online, users must create a digital account, which requires sharing personal information such as name, phone number, email ID, location, and so on.

This data is the lifeblood of any business, as it is used to arrive at insights that help businesses to offer more personalized customer services as well as to improve their products and services. However, bad actors exploit this information and fuel activities that can range from harmless pranks to more sinister crimes with serious social implications.

To protect consumer data from possible exposure, directives such as the General Data Protection Regulation (GDPR) mandate businesses to ensure privacy of their customers’ personal information. In order to protect the privacy of consumer data, businesses use data anonymization techniques. Some of the commonly used data anonymization techniques include:

Data encryption: Encryption is achieved using algorithms that convert data into an unreadable format or code, which renders it unusable in the altered state. This technique is generally used to protect data at rest and in transit, where data is not needed immediately—for example storage or network links. However, since it is reversible, encryption offers the flexibility to re-identify the files when needed using a relevant decryption key.

Pseudonymization: This technique allows businesses to manage and de-identify data by replacing private identifiers with pseudonyms—and hence the name pseudonymization. However, this process is reversible and does not remove all identifiers. As a result, the accuracy and integrity of data is preserved, which allows the formatted data to be used for training or testing purposes.

Data masking: Using the masking technique, businesses can create a mirror version of the data they possess. The mirrored data can further be processed using encryption and shuffling or substitution of characters and words that prove useful in preserving the format requirements of an application—such as a shopping bill where the credit card information is masked out.

Data swapping: The technique where attributes in a dataset are rearranged in such a manner that they do not match with the original records is known as data swapping or shuffling. Data swapping is an irreversible technique such that fetching original data is nearly impossible.

Substitution: In this technique, the data in a column of a database is replaced with fake data in order to rule out identification of an individual. This helps preserve the integrity of the original data.

Nulling out: Complete removal of sensitive data from a data set is called nulling out.

Variance: This technique is especially useful when dealing with numerical data and alters the data value in a given column by a certain percentage.

Perturbation: Alteration of data by adding noise and rounding values is called perturbation.

Synthetic data: In this technique, artificial information, which has no semblance to real data, is created using algorithms. In order to create synthetic data, statistical models are created using the patterns from the original dataset.

Generalization: When the data is deliberately rendered less precise by reducing the granularity of data, retrieving an individual’s information becomes difficult. Usually, the data is modified by using broad ranges instead of individual data values.

Blurring: Blurring is also a precision-reducing technique, but it relies on approximation of data values.

Directory replacement: In this technique, the names of the individuals are changed while other related information is stored separately. In the absence of this stored information, the individual cannot be identified with certainty.

Custom anonymization: Every business faces a unique challenge with data and may, therefore, choose to combine multiple techniques or applications to create their own anonymization technique. This is called custom anonymization.

The article has been written by Neetu Katyal, Content and Marketing Consultant

She can be reached on LinkedIn.