Demystifying Pseudonymization and Anonymization 

 Introduction  

Privacy laws, including the EU General Data Protection Regulation and the Digital Personal Data Protection Act, have ignited both interest and necessity in the adoption of technologies for securing data.  In a world where data is gold and a cornerstone for business success, its security and privacy become crucial. This is where technologies like data Anonymization and Pseudonymization become the beacon of hope. Business operates on consumer trust and one of ways to reinforce the trust is through safeguarding the right to privacy of consumer.

What is Pseudonymization?

It is one of the technologies used to operationalize data protection. It is defined in Article 4(5) of the GDPR to mean “processing of personal data in such a way that the data can no longer be attributed to a specific data subject without the use of additional information.”   In other words, this technology helps in obscuring the identity of the individual to a limited extent. Therefore, the individual can still be identified with additional information or identifiers, which is why such data falls within the purview of GDPR. The risk of Re-identification, diminished data quality and cost complexity are some of the concerns that haunts in implementation of this technology.

 
  
What is Anonymization?   

Anonymization turns the data's status into an anonymized version, so that the data can never identify the owner.  In other words, the company removes all the links between the data and the owner. This mechanism is a gamechanger technology that safeguards not only the data owner but the company as well. When the organisation installs the anonymisation technology in its data base, it becomes outside the scope of GDPR or any other data protection laws. 1Having said that, this technology of anonymisation is difficult. The complete disconnect between the data and the owner might not be fruitful for the organisation, however, it works best for research and statistical purposes.  
 

Importance

The installation of the above process significantly reduces privacy compliance risks and improves customer experience. It creates brand value and insulates the company from risks of breaches and compliance. By integrating this technology, the company mitigates the risk to the data subjects and assists them in complying with the regulatory obligations. With the increased use of digital media and globalisation the process of anonymisation gives the organisation ample security when the data is transferred within or beyond its territorial limits, and hence mitigates risks of breaches, leaks, modification, unauthorised use or access. Operating in the times where data is gold, guarding personal data from breaches is paramount. This is where technologies like pseudonymization and anonymization act as bulwark, by making certain that even in time of breach or unauthorised access, the data is shrouded in anonymity. Where businesses thrive on data -driven analytics, techniques like synthetic data generation attempts to retain the functionality of the data post anonymisations.  

How to implement?  

There are various ways in which data can be pseudonymised or anonymised. Different companies follow different techniques depending on their data set, volume, categorisation, purpose etc. Some of the ways are- 

Data Scrambling- It is where systems jumble the letters and mixes its variant. For example, use of hashing and encryption techniques.  

Data Masking- This technique is rampantly used in fintech companies and payment industry’s where there is extensive card data processing. These companies to comply by the PCI DSS standards and ensuring data protection, mask the card numbers to an extent that becomes safe for only analytical purposes. This technique keeps the sensitive card details of the data subjects secure at the same time helps companies to provide best customer experience. This technology masks or hides certain information by using random data into the sets. This is a technique that cloaks the data and ensures data anonymity. The data subject’s anonymity is secured and not in jeopardy.  

Noise Infusion- As the terminology indicates, there is an integration of noise into the data sets, this ensures obfuscation to the value of the original data sets. This technique maintains a fruitful balance of ensuring that the data is anonymous yet can be used for constructive business analysis.  

Directory Replacement- This technique pseudonymises the data. Herein, the company uses identification number and assigns the same to the data subject. For example, the system uses a customer id to identify the individual and stores all the data that correlates to the data subject. They separately store a personal identification number to help in redirecting the system to pin out the data subject. If the additional information is not stored, the data subject can never be corelated to its personal data, hence achieving anonymity.  

Conclusion  

It is important to understand that no technology or technique can guarantee complete security. Implementing these techniques is difficult at the same time necessary to ensure gold standard of protection for all kinds of processing activities that a company undertakes. Companies must access their business and accordingly implement the technology, so that they can ensure high standards of data protection at the same time efficiently run their business.