Advantages and Disadvantages of Anonymized Data

What is anonymized data, and why do enterprises require it? Learn about its key types, benefits, challenges, and a new approach based on business entities.

Table of Contents

What is Anonymized Data? 
Types of Anonymized Data 
Benefits of Using Anonymized Data
Challenges of Using Anonymized Data
Take a Business Entity Approach to Anonymizing Data 

What is Anonymized Data?

Anonymized data is data that has been altered in a way that makes it impossible, or very difficult, to identify the person associated with it. The process of data anonymization obscures or removes PII (Personally Identifiable Information) from a dataset while ensuring the data remains functional for software business analytics, customer support, development and testing, and other use cases.

Data anonymization is a crucial capability for enterprises today. As the amount of data companies collect and store increases, and as data privacy regulations expand, the risk of a data breach or compliance violation is greater than ever. Regulatory noncompliance can lead to costly penalties, years of litigation, brand damage, and customer turnover.

In this article, we’ll cover key types of benefits and challenges associated with using anonymized data, the most common types of data anonymization tools, and the advantages of taking a business entity-based approach.

Types of Anonymized Data 

Here’s a brief overview of the 6 main types of anonymized data:

1. Masked data
   Data masking is the process of obscuring or replacing real PII with obfuscated, yet statistically equivalent, data. Masking data is one of the most secure ways to anonymize data because the original data cannot be identified, or reverse engineered. It’s commonly used to support compliance with consumer privacy regulations and conceal financial information, PHI (Protected Health Information), and intellectual property. 

2. Pseudonymized data
   Pseudonymization anonymizes data by replacing identifying information with a pseudonym. PII that is commonly replaced could include names, addresses, and social security numbers. Pseudonymization reduces the risk of PII exposure or misuse, while still allowing the dataset to be used for legitimate purposes. This type of data anonymization is also reversible, and is often used in combination with other methods, such as data masking vs encryption.

3. Aggregated data
   Data aggregation is the process of combining data collected from many different sources into a single view, so the resulting data cannot be traced back to specific individuals. In this method, individual records are grouped together based on shared characteristics, such as age, gender, location, purchase behavior, or any other criteria. Once the data has been aggregated, it can be analyzed without identifying individual records. Data aggregation can be done on categorical data, numerical data, and text data. It can also be performed on data that has already been pseudonymized or masked to add an extra layer of protection.

4. Randomly generated data
   In this approach to data anonymization, data is shuffled in order to obscure sensitive information. Data shuffling arbitrarily reorders first and/or last names, street addresses, etc. within the same dataset. It can be applied to an entire dataset, or to specific fields or columns in a database. A random data generator is often used along with data masking tools or data tokenization tools. It’s commonly employed when assigning groups in clinical trials, to ensure that subjects are chosen and assigned to treatment groups at random.

5. Generalized data
   Data generalization produces anonymized data (such as addresses or ages) by replacing specific data values with more generalized values. This method can also replace specific types of data with broader data categories. For example, a specific address can be replaced with a generic label, such as downtown, midtown, or uptown. Similarly, the age 35 can be generalized to an age group called 30-40 or millennials.

6. Swapped data
   Data swapping replaces real data values with made up, but realistic, values. It’s similar to the random data generator, but instead of shuffling the data, it replaces the original values with new, synthetic ones. For example, a real name, say Sarah Rogers, can be swapped with a fictitious one, like Jessica Smith. Or a real address, like 186 South Street, can be swapped with a made up one, like 15 Parkside Lane.

Benefits of Using Anonymized Data 

Enterprises that anonymize data in production and non-production environments can take advantage of many benefits, including:

1. Enhanced data privacy and security
   Anonymizing data makes it easier to prevent unauthorized users from accessing or mis-using personal information – such as names, addresses, social security numbers, financial information, PHI (Personal Health Information), and more – when it’s moved from production to non-production environments. This helps enterprises comply with data privacy regulations such as APPI, CCPA/CPRA, DCIA, GDPR, HIPAA, PDP, and SOX, and minimizes the risk of data breaches and cyberattacks.

2. Improved data analysis
   Even after data is anonymized, it can still be used for analytics, deriving business insights, supporting decision-making, and enabling research. With anonymized data, enterprises can perform in-depth analysis on large volumes of data while preserving individual privacy.

3. Cost savings
   Anonymized data is typically less expensive to collect, store, process, and secure than raw data. This can help enterprises reduce costs associated with data management and analytics.

4. Greater collaboration
   It’s far safer and easier to share anonymized data with 3rd-parties, such as analysts, researchers, and vendors, as well as other companies. By anonymizing data, enterprises can boost collaborations that enable them to produce value and insights that might not otherwise be accessible.

5. Increased trust and reputation
   Today’s consumers are increasingly concerned about data privacy and security. The use of anonymized data can improve a company’s reputation as a responsible custodian of personal customer data and help them foster long-term customer relationships.

Challenges of Using Anonymized Data 

Despite its advantages, the process of anonymizing data and working with anonymized data itself poses certain challenges that are worth anticipating. For example:

1. Risk of re-identification
   The risk of re-identifying the person with whom sensitive data is associated may remain, depending on the type of data anonymization functionality is in place. For example, attackers might attempt linkage attacks, which cross-reference anonymized data with publicly available records, to re-identify individuals. Or they might use an inference attack, which relies on attributes like age and gender to infer identity. Certain machine learning algorithms can also effectively analyze patterns found in anonymized datasets, making it easier to re-identify the person behind the data.

2. Reduced data utility
   Anonymized data may result in a loss of utility, because sensitive or unique data points are removed or obfuscated. Significantly changing some of the information can make it difficult to draw accurate insights from the data or use it for analytical purposes.

3. Complying with international privacy regulations
   Different regions and countries uphold different regulations for anonymized data. Determining how to navigate and comply with these requirements – especially if an enterprise operates in multiple jurisdictions, spanning many different privacy regulations – can be a major challenge.

4. Integrating with AI and ML models
   Anonymized data lacks the richness of raw data. As a result, it may be less appropriate for training machine learning and AI algorithms, which, depending on the use case, rely on detailed and accurate data to learn and make predictions.

Take a Business Entity Approach to Anonymizing Data 

A traditional or fragmented approach to anonymizing data can make it difficult to ensure relational consistency and accuracy across different datasets and data stores. With the entity-based data masking technology, data teams can anonymize data quickly, efficiently, and reliably, while preserving functionality for a variety of use cases.

A business entity solution integrates and organizes fragmented data from multiple source systems according to data schemas – where each schema corresponds to a business entity (such as a customer, vendor, or order).

The solution anonymizes data based on the business entity, and manages it in its own, encrypted Micro-Database™. Each Micro-Database™ is either stored or cached in memory. This new, patented technology enables highly effective data anonymization at unprecedented speeds.