Data Anonymization: Balancing Data Privacy And Data Utility

Data Anonymization: Balancing Data Privacy And Data Utility
6 min read

Data Anonymization is a crucial tool in cybersecurity that protects data and its usage. It is effective in various sectors of many organizations in today's globalized landscape.

While most organizations aim to protect data during and after usage, creating an effective balance between privacy and utility is a major challenge.

In this article, we will discuss several techniques that are useful in striking a balance between data privacy and utility. But first, let us understand the term Data anonymization.

What is Data Anonymization?

Data anonymization is the process of protecting sensitive and confidential information. It is done by strategically deleting or encoding relatable information that connects Individuals to the stored data.

It aims to preserve the privacy of individuals or organizations while maintaining the authenticity of the collected and exchanged data.

Several companies incorporate lots of regulatory and essential protocols, to ensure that data and intellectual properties are privatized and protected from unauthorized bodies and cyber attacks. 

Some of these processes include the use of effective data masking tips, data encryption, backup and recovery, access control, and many other procedures to keep individuals' and organizations sensitive and useful data away from breaches.

However, data must be utilized to carry out various operations and ensure the effective functioning of an organization. This often requires breaking some data privacy protocols to enable usage.

Define Data Privacy and Utility

Data privacy, as the name implies, refers to the aspect of data protection that involves proper storage, retention, access, and security of sensitive data.

This enables organizations or owners of certain sensitive information, to control who can gain access to it and who can not. This goes on to limit the number of people who can share or transfer this sensitive information, without consent. 

Data utility encompasses how useful a set of data is for a specific task and how it can be used. In simpler terms, data utility gives value to a particular data set. 

Data privacy is important to keep sensitive and intellectual company property away from cyber-attacks and unauthorized third parties. But teams and employees require access to these data to carry out various tasks.

Striking a balance between data privacy and utility is one complex and critical challenge in this evolving digital age. 

This is complex because while you aim at protecting data from attacks and loss, individuals and employees within your organization require access to these data to carry out various tasks.

However, despite how complex the process is, data anonymization is an effective technique that creates a functional balance between these two factors. 

By applying data anonymization procedures, your organization can comply with strict data privacy regulations, particularly those that require the protection of personally identifiable information (PII).

PII include medical reports, financial and contact information.

While simply deleting identifiers from the data may not be enough for adequate data privacy, Below are some effective techniques of data anonymization that can be applied. They include:

Effective Data Anonymization Techniques

  • Data Masking: 

This refers to the protection of data with modified values. Data anonymizing is done by creating an inauthentic version of a database and implementing several alterations. 

By masking sensitive data, the authentic data set is protected while the inauthentic version is without value to unauthorized bodies.

  • Pseudonymization: 

This tool is effective in the de-identification of data. It substitutes private identifiers with forged pseudonyms.  

For example, if a data set contains "Ann Brown '' as its true identifier, pseudonymization swaps it with the "Mary Drew" identifier. 

This process maintains data precision and confidentiality. It also allows the changed data to be used for training, creation, analysis, and testing purposes. But maintains the overall privacy of the data set.

  • Generalization: 

This involves the intentional removal of some data to make it less identifiable. This modifies data into a series of ranges with sensible limits. 

Generalization aims at removing identifiers while maintaining data accuracy.

  • Data Permutation: 

This technique reorganizes dataset column values in a way that they do not match the original information. 

Swapping sections of data sets that include date of birth can be very effective in data anonymization.

  • Data Perturbation: 

This modifies the original dataset slightly by implementing the process of round numbering and random noise. The set of values must equal the noise.

  • Synthetic Data: 

This process involves the generation of information with no actual connection to any actual case. 

This data is used to develop artificial data sets without any need to modify or use the original dataset and breach data privacy and protection.

However, while data anonymization techniques may be effective in the protection of data, attackers can still penetrate using data de-anonymization procedures to retrace techniques of anonymization. 

This is because data flows across through many sources which are open to the public and can facilitate cross referencing for de-anonymization. Which makes data anonymization complex and challenging

To ensure complete data anonymization, your organization must regularly assess the level of anonymization and carry out more protective measures when necessary.

For an effective balance between privacy and utility using data anonymization,  your organization must consider the following:

  1. How sensitive the data they are collecting is and how it is handled
  1. Ensure compliance with regulatory requirements such as General Data Protection Regulation (GDPR) or Health Insurance Portability and Accountability Act (HIPAA), whichever is applicable to your organization
  1. Carry out Risk evaluation and data assessment
  1. Educate individuals' on how their data is used and their right to consent or withdrawal of consent at any time.
  1. Understand the impact of a possible re-identification if data is de-anonymized

The Bottom Line

Data Anonymization is a useful technique in ensuring data protection and privacy. It also plays a crucial role in striking a balance between privacy and utility.

Every organization must consider several factors before anonymizing any individual data. In addition, they must regularly access their anonymization techniques to ensure it is effective.

By applying most of the listed requirements, data privacy and utility is balanced and anonymization of data can be effectively implemented. 


Mian Shoaib 2
Joined: 4 months ago
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