Table of Contents

    Table of Contents

    Data Anonymization vs Data Masking: Definitions/Use Cases

    Amitai Richman

    Amitai Richman

    Product Marketing Director

    Data anonymization removes classified, personal, or sensitive information from datasets, while data masking obscures confidential data with altered values. 

    Table of Contents


    What is Data Anonymization?  
    What is Data Masking? 
    Data Anonymization vs Data Masking Use Cases 
    What’s Next for Data Anonymization and Data Masking? 
    Data Anonymization vs Data Masking Based on Business Entities  

    What is Data Anonymization? 

    Data anonymization reduces the risk of sensitive data disclosure – both accidental and malicious – by removing personally identifiable information from datasets. By doing so, data anonymization tools enable organizations to use their data for wider purposes, without violating various data privacy regulations. 

    Nearly every organization that needs to collect, store, manipulate, or send sensitive data, uses data anonymization techniques. The solutions that anonymize data are generally configurable – adjusting the level and type of anonymization to the relevant business, data, and applicable regulatory regimes. 

    One advantage of anonymized data is that it usually remains functionally intact – enabling effective data analysis and manipulation for marketing, customer service or other uses. At the same time, the sensitive values in an anonymized dataset are irreversibly obfuscated – names, addresses, telephone numbers, etc. Because of this, regulatory regimes like the European Union’s General Data Protection Regulation (GDPR) don’t consider a correctly anonymized dataset as Personally Identifiable Information (PII). 

    What is Data Masking? 

    Data masking is the process of hiding sensitive, classified, or personal data from a dataset, then replacing it with equivalent random characters, dummy information, or fake data. This essentially creates an inauthentic version of data, while preserving the structural characteristics of the dataset itself. Data masking tools allow data to be used for purposes like user training and software testing – protecting the actual sensitive data while offering a functional substitute for critical organization usage. 

    Frequently used in organizations where different business domains have different data needs (for example, customer service agents that don’t need to see customer credit card numbers), data masking hides sensitive data on a need-to-know basis – enhancing data security and privacy compliance. Data masking works by substituting sensitive values in a dataset with randomized values using different data manipulation or shuffling techniques. Because data masking is a reversible process, it’s still considered PII under many regulations, like GDPR. 

    Data Anonymization vs Data Masking Use Cases 

    There are numerous use cases for data anonymization and data masking – many overlapping. Some of the most prominent include: 

    Data Anonymization 

    • Facilitating collaboration – When organizations need to share confidential information, privacy considerations can be a huge impediment. For example, if a hospital needs to share medical outcomes with a research institute, the data must first by anonymized – all fields that could possibly identity an individual must be irreversibly obfuscated, while still preserving the integrity of the dataset for research purposes.

    • Enabling insights – Effective data anonymization can help organizations derive insights from customer data, even when customer consent for using their data is not forthcoming. By permanently anonymizing the sensitive values in a dataset, organizations can unlock the value hidden in customer data without violating customer privacy. This enables improved product recommendations, more personalized ads, new product ideas, and enhanced online services and user experience.

    • Reducing financial fraud – Financial services companies are required by regulations like GDPR to obtain customer consent to analyze data – even when the goal of that analysis is mitigation of potentially fraudulent activity. Data anonymization eliminates this hurdle – allowing financial services organizations to better combat fraud without privacy constraints.  

    • Improving public policy – Governments are subject to data privacy regulations too. Yet use of data collected about citizens can measurably improve policing and other public policy initiatives. For example, crime can be more effectively predicted using anonymized data gleaned from current crime statistics and social media. Similarly, national statistics offices can make more accurate assessments of public policy issues based on actual – yet anonymized – data.  

    Data Masking

    • Achieving and maintaining compliance – Data privacy regulations like GDPR, HIPAA, GLBA, PCI DSS and others, mandate masking data like PII and other forms of sensitive information, like medical or financial records.

    • Controlling internal access – Organizations use various types of data masking internally to make sure that staff who don’t require access to sensitive information won’t be able to access it. A simple example of this is masking the last four digits of a credit card number for non-financial staff.

    • Accelerating development – DevOps needs functional datasets for its continuous testing efforts, but manually removing PII can be tedious and time-consuming, which slows version releases. Dynamic data masking allows for faster and more efficient development by enabling shift-left testing and the creation of synthetic test data

    What’s Next for Data Anonymization and Data Masking? 

    There is a new generation of data anonymization and data masking solutions that are better able to ensure data privacy and regulatory compliance given today’s complex data structures, hybrid cloud/on-prem environments, and increasingly sophisticated cyberattacks. This new generation of Privacy Enhancing Technologies (PETs) were born of the worlds of encryption, statistics and AI, and include: 

    • AI-generated synthetic data, which retains the statistical properties of a dataset without any of the dataset’s original datapoints.

    • Homomorphic encryption, which enables performing analytics on encrypted data without ever decrypting it.

    • Federated learning, which enables Machine Learning (ML) models to be trained and operated locally on devices, so the data doesn’t have to travel.

    Data Anonymization vs Data Masking Based on Business Entities  

    The most effective and technologically advanced methodology for the anonymization of data, or for adhering to data masking best practices, relies on the business entity approach to data masking challenges. A business entity corresponds to a customer, device, invoice, or anything else that’s important to the business. All the data associated with a specific entity (a single customer) is stored and accessed from an individually encrypted Micro-Database™. With entity-based data anonymization or data masking software that leverages intelligent business rules, organizations are better able to maintain productivity, while still ensuring privacy compliance. 

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