In today’s massively digitalized world, data translates to power. Just like in the James Bond movies, everyone does anything to hold on to their data, thus their identity. They don’t want to be known or seen. All these precautions and procedures employed in keeping these identities secret is what anonymization is all about.
Personal privacy is a topic of growing concern for businesses and consumers alike. With more and more services going online and with the rise of personalization in marketing and product recommendations, businesses are always looking for smarter ways to use data.
But these innovations cannot ignore the security of data storage and privacy protection guidelines from GDPR or CCPA. If brands are going to leverage customer data, they have a responsibility to protect it, and data anonymization is what they need to keep it safe and well.
No big-name company brand wants to be a negligent or accidental offender. What’s more, a guarantee for data privacy compliance is a sure way to build consumer trust, just like a data breach can be a dead-cert way to lose a hard-won positive status.
Data Anonymization: how does it work?
Data anonymization is the process of permanently hiding data in obscurity. It removes any identifiable feature that links a data set to the owner of the data. Technically, it’s a process of altering data – by encrypting key identifiers – to obscure identification and provide faster data movement between systems. For example, a financial institution may encrypt the address, the name, and other personally identifiable information (PII) of their users while transferring a compilation of their daily savings. In doing so, limited exposure to the data prevents attackers from tracing them to actual users.
At the same time, attackers can find a way around anonymization: by using relevant sources to match data to the subjects and key identifiers, they can de-anonymize information. It seems like a possible thing, so why wouldn’t they do it? However, there are different anonymization practices and procedures that can prevent the possible de-anonymization of these data sets.
There are a number of ways that personally identifiable information (PII) like credit card details, names, mobile numbers, email, or physical addresses can be disassociated from their individual owner. One of them, and perhaps the most effective, is using an AI-based anonymization service from Pangeanic to identify PII and replace sensitive data using a series of anonymization techniques.
Techniques of Data Anonymization
- Data masking
As the name suggests, data masking refers to the process of hiding and giving data sets another apparent identity. Data masking involves creating a duplicate (mirror version) of a data set with the purpose of hiding the original data sets among inauthentic ones. Custom anonymization solutions like Pangeanic can help create different versions of the same data and shuffle it with the original version that is subjected to protection. When it comes to de-anonymization, the data masking technique makes it more difficult for attackers.
- Pseudonymization
This technique involves replacing potential PII identifiers with fake ones. This is perhaps one of the best techniques for hiding data while maintaining the accuracy of statistics and value. Pangeanic is replacing identifiable critical data with pseudonyms, a method that’s often applied in demo operations and analytical training.
- Generalization of data
Data generalization embodies the underlying principle of data anonymization. This practice involves removing some data sets in order to make it less recognizable.
- Data swapping
The data swapping technique is as simple as it sounds. This practice shuffles data sets to re-arrange them in an unrecognizable way. That way. There would be no resemblance between the resultant records and the original database.
- Data perturbation
The data perturbation technique is often found in numerical data inputs. To do this, data anonymization services round up the data sets with a specific operation or value.
- Synthetic data
Just as the name implies, synthetic data sets are sets of artificial data created by AI with no connection whatsoever to existing events. Some data administrators and cybersecurity experts consider this anonymization technique more efficient than making alterations to the original data sets.
Advantages of Data Anonymization
1. Protects businesses against the potential loss of market share and trust
Without a data anonymization method, companies wouldn’t understand or enforce their duty to secure sensitive, personal, and confidential data. In a world of overly complex and ever-changing data protection mandates, data anonymization protects companies against the potential loss of market share and trust.
2. Protects customers against data misuse and insider exploitation risks
Customers and clients who entrust companies with their personal data are susceptible to data misuse and insider exploitation risks that result in the failure of GDPR or CCPA compliance. Data anonymization practices ensure that companies fully understand and enforce their duty to secure confidential, sensitive, and personal data.
3. Improves governance and consistency of results
Clean and accurate data, allows companies to use services and apps and maintain privacy. It drives digital transformation by delivering protected data for use in creating new market value.
Wrapping up
From the overall appraisal, businesses who really value their hard-earned positive reputation and their users’ privacy & safety should consider anonymizing critical data sets. So far, a rapidly increasing number of data breaches, combined with global attention to privacy mandates, means that companies that feed on user data should make data privacy a top-of-mind priority. This might not be easy, but it’s an absolute must if you want to maintain regulatory compliance, and market share and ward off attackers.
Written by Adam Eaton
