Data Anonymization

Article byShrestha Ghosal
Edited byAshish Kumar Srivastav
Reviewed byDheeraj Vaidya, CFA, FRM

Data Anonymization Meaning

Data anonymization is when corporations alter, remove, or encrypt an individual or a company’s data. The data anonymization process aims to safeguard an individual or a company’s personal information while conserving the plausibility of the collected or exchanged data. Furthermore, this process is instrumental in preserving their private activities.

Data Anonymization

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This process is called “data de-identification” and “data obfuscation.” Data de-identification helps facilitate the transfer process across boundaries while minimizing the threats of disclosing personally identifiable information (PII) like financial information, address, and contact details.

Key Takeaways

  • Data anonymization is the process of encoding, modifying, or removing data or data attributes to protect privacy.
  • It is a method corporations engage to safeguard a company or an individual’s personal, private, and confidential information.
  • This process is also known as data de-identification and data obfuscation. It is a valuable tool for storing and exchanging significant amounts of data across boundaries while reducing the threats of unintended disclosure.
  • It also protects data from being misused by any third party. It prevents hackers and scammers from conducting identity thefts.

How Does Data Anonymization Work?

Data anonymization is when a corporation deletes, modifies, or encodes an individual or a company’s data, intending to preserve their personal information. The data anonymization process ensures that the collected or exchanged data’s authenticity remains intact without compromising an individual or company’s privacy. Several business processes require data transfer, which may occur across various boundaries, like from one department or company to another. Companies implement data anonymization methods to reduce the risks of disclosing confidential and private information during data transfer.

Corporations require vast amounts of data like personally identifiable information of their customers, other companies, and even the general public for their business processes. This includes address, contact details, financial information, health records, etc. Therefore, they collect, exchange, and store the information for further processing. In the current scenario, e-commerce and digital media businesses also gather and exchange public data. They use this data to provide customized services to their users.

Additionally, the information received is valuable for preparing tailor-made advertisements to attract consumers. Furthermore, social media sites use consumer data for their algorithms.

In such cases, businesses must protect the collected data. A breach of privacy could lead to unwanted parties like hackers and scammers getting a hold of this sensitive information and misusing them to cause identity thefts. Therefore, most companies have strict policies on maintaining the confidentiality of such private information. However, anonymized data may not remain safe over time. Data de-anonymization is a reversal process by which formerly anonymized data is re-identified. This process involves cross-referencing with other data sources to determine the anonymized data source.

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The data anonymization methods are as follows:

#1 – Synthetic Data

In this method, synthetic data is created with the help of algorithms without connection to reality. In this method, fake data is made instead of altering actual data, which helps preserve privacy. Several mathematical and statistical methods, like medians, linear regressions, and standard deviations, are applied to the original data to create this data.

#2 – Data Swapping

This method involves switching or shuffling the data attributes to protect data privacy. It is also known as data permutation and data shuffling. Here the data columns are rearranged so there is no match between the resulting and original data.

#3 – Data Perturbation

The original data is marginally modified using random noise or rounding methods in this method. This method helps protect data privacy while keeping it credible. However, the rounding values must be carefully selected. If the base value is too small, the data may not be adequately anonymized, and if it is too large, the data might lose its plausibility and become unusable.

#4 – Generalization

This method involves deleting some data attributes to make them less recognizable. In addition, the original data can be tweaked by eliminating specific identifiers. As a result, privacy remains intact without removing the data’s accuracy.


The data de-identification types are as follows:

#1 – Medical Research

Healthcare professionals and researchers use data anonymization to collect and examine data about a prevalent disease in a specific area. They are required to comply with HIPAA standards and maintain data confidentiality.

#2 – Software And Product Development

Software and product developers must gather actual data and use it to develop products and software that would address and solve real-life problems. In addition, they need this data for testing and improving existing products and software. Thus, they must keep the sensitive data secure by anonymizing them.

#3 – Business Operations

Several business operations require sensitive data of consumers and employees. They use the data to provide personalized services to customers. In addition, employee data is used to optimize employee safety, enhance productivity, and increase performance. Anonymizing such sensitive information would help the organization gather valuable data without making the individuals feel like they are being exploited, tracked, or monitored.

#4 – Marketing Operations

Various marketing operations require consumer data to improve their processes. The collected data provides valuable insights to the companies about customer preferences. These insights create customized advertisements and user experiences to lure customers. The data is anonymized to maintain confidentiality without compromising the data authenticity.


Let us understand this concept with the following examples:

Example #1

Suppose Divine Electronics is a company that sells electronic goods. They collect data from their customer for after-sales services. For example, if the goods they sell require repair, the company gathers data from the reporting customer and sends their representatives to the customer’s place. As a result, they collect and store enormous amounts of sensitive data. Then, they alter that data by shuffling and rearranging specific attributes. They do this to protect the customer’s identities and their personal information like their contact numbers, addresses, and billing details.

Example #2

America’s Federal Trade Corporation got stern about the lack of data anonymization of sensitive information. They announced that several companies that claim to anonymize data deceive their customers by breaching their privacy. They collect information and monetize it. The consumers are not even aware of their data being sold. This unprecedented intrusion has been criticized the by the corporation. They planned to enforce more stringent rules.


The data anonymization benefits are as follows:

  • Data de-identification enables storing and exchanging sensitive information without compromising an individual or company’s privacy. This process helps to provide a sense of security to the public. As a result, there is no potential loss of trust or market share.
  • One of the most critical data anonymization benefits is safeguarding information from misuse. Several scammers and hackers use the data to exploit the public or cause identity theft. Anonymized data prevents them from accessing the original data and breaching privacy.

Data Anonymization vs Data Masking vs Pseudonymization

 The differences are as follows:

  • Data Anonymization: This process involves encrypting, eliminating, and altering collected or exchanged data to maintain an individual or a company’s confidential information.
  • Data Masking: Data masking is a process where an explicitly authorized party can access only a specific part of the original data set. Certain pieces of data are kept hidden. This process adds another layer of security to the data de-identification process.
  • Pseudonymization: The pseudonymization process involves replacing confidential data identifiers with fake identifiers or pseudonyms. This process does not eliminate identifiers like data de-identification. Instead, the specific identifiers are merely replaced with false information to maintain confidentiality.

Frequently Asked Questions (FAQs)

Does data anonymization require consent?

No, data anonymization does not require consent. It is a tedious and unfeasible task to obtain explicit consent for the anonymization process. Furthermore, in some instances where huge data chunks are collected, such as machine learning, research, and big data analytics, securing consent from each concerned individual would be impossible. Therefore, performing this process does not need any permission.

What are the issues with data anonymization?

The General Data Protection Regulation, or the GDPR, requires that websites ask users for consent to collect personal information like device ID, cookies, and IP addresses. Gathering anonymous data and eliminating the crucial identifiers from the database would restrict a company’s ability to gain insights from the data. As a result, it would not be possible for companies to provide personalized ads and services to their consumers or to enhance user experience.

What are the guidelines for data anonymization?

The guidelines for data anonymization state that an ample amount of information must be removed or altered from the initial data set so that it cannot be used or identified by the data controller or any third party involved.

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