Data Education Center: Data Masking vs. Encryption

Protecting sensitive information like personally identifiable information (PII) and financial data is crucial for compliance and consumer trust. Two key methods employed for data protection are data masking and encryption.

While both serve the purpose of safeguarding sensitive data, they function in distinct ways and cater to different needs. This article delves into the intricacies of data masking and encryption, highlighting their functionalities, types, importance, and key differences.

What is Data Masking and How Does it Work?

Data masking, also known as data obfuscation, is a technique that replaces sensitive data with substitute values, rendering it unidentifiable in its original form.

Imagine masking a credit card number by replacing the middle digits with asterisks. The masked data (e.g., 1234 **** **** 5678) retains its format and structure, making it usable for testing, development, and analytics purposes, while the actual sensitive information remains concealed.

Here's how data masking works:

Identifying Sensitive Data

The first step involves pinpointing the specific data elements that require protection. This could include PII like social security numbers, credit card details, and medical records, or financial data such as account numbers and transaction history.

Masking Techniques

Once sensitive data is identified, various masking techniques can be applied. These techniques fall into two primary categories:

1. Character Masking: This involves replacing sensitive characters with alternative characters, such as asterisks, random characters, or predefined patterns.

2. Substitution Masking: This replaces sensitive data with substitute values that maintain a similar format and structure. For example, a social security number could be masked with a randomly generated number that follows the same format (XXX-XX-XXXX).

The choice of masking technique depends on the specific data being masked and the desired level of protection.

Unlike encryption, masked data remains usable for various purposes. This enables developers and testers to work with realistic data sets without compromising sensitive information. Data analysts can also extract valuable insights from masked data for research and analytics purposes, while maintaining data privacy compliance.

What is Encryption and How Does Encryption Work?

Encryption serves as another critical tool in the data security arsenal, functioning distinctly from data masking. While data masking obscures sensitive data, encryption scrambles it into an unreadable format, rendering it completely inaccessible without a specific key.

Imagine a locked safe – the data is securely stored within, but only those with the correct combination can access it.

Here's a breakdown of how encryption works:

Plaintext vs. Ciphertext

Data in its original, readable form is referred to as plaintext. Encryption algorithms transform this plaintext into ciphertext, an unreadable format that resembles gibberish.

Encryption Algorithms

Complex mathematical algorithms are employed to perform the encryption process. These algorithms act as the "scrambling tools," transforming the data into ciphertext. Popular encryption algorithms include AES (Advanced Encryption Standard) and RSA (Rivest–Shamir–Adleman).

Encryption Keys

Encryption relies heavily on keys – unique digital sequences that act as the "locks" for the encrypted data. The encryption key is used to scramble the data, while a separate decryption key is required to unlock and access the original information.

There are two primary types of encryption keys:

Symmetric Keys

In symmetric key encryption, a single key serves both encryption and decryption purposes. This method is efficient for encrypting large datasets but requires secure key management to ensure the key remains confidential.

Asymmetric Keys

Asymmetric key encryption, also known as public-key cryptography, utilizes a pair of keys – a public key and a private key. The public key is used for encryption, while the private key is kept confidential and used solely for decryption. This method offers enhanced security, as the private key never needs to be shared publicly.

The choice of encryption algorithm and key type depends on various factors, including the sensitivity of the data, performance requirements, and the desired level of security.

Types of Data Masking

Data masking can take on various forms, each tailored to specific needs and use cases. Understanding these types can help organizations choose the most suitable option for their data security strategy.

Here's a breakdown of the commonly used data masking techniques:

1. Static Data Masking:

This method involves applying a permanent masking transformation to sensitive data before it is stored or shared. A masked copy of the original data is created, with the sensitive elements replaced by substitute values.

• Benefits:

  • Efficiency: Static masking is a relatively simple and efficient process, making it suitable for large datasets.

  • Compliance: It helps organizations comply with data privacy regulations by ensuring sensitive data remains masked throughout its lifecycle.

• Examples:

  • Replacing social security numbers with randomly generated numbers following the same format (XXX-XX-XXXX).

  • Masking email addresses by replacing identifiable characters with generic symbols (e.g., [email address removed] becomes [email address removed]).

2. Dynamic Data Masking:

This technique applies masking transformations for data in transit; i.e., only at the point of data access. Sensitive data is still stored as plaintext in the source database, but query results produce redacted output for that data when the application user is not authorized to see the original values.

• Examples:

  • Masking credit card numbers in real-time during online transactions, displaying only the last four digits to the user.

  • Implementing dynamic unmasking for PII masked in production environments, ensuring sensitive data remains protected at rest.

3. Deterministic Data Masking:

This method utilizes a pre-defined algorithm to consistently replace sensitive data with the same unique masked values; i.e., the same input value will always be masked to the same output value. This maintains referential integrity, and when encryption is used, the masking can be reversed if needed.

• Benefits:

  • Predictability: Deterministic masking simplifies data analysis and reconciliation, as the masking rules are consistent and predictable.

  • Auditing: It facilitates easier auditing and tracking of masked data, as the original values can be reconstructed if necessary.

• Examples:

  • Replacing all social security numbers in a dataset with a specific sequence of numbers, allowing for identification of the masking process while maintaining data privacy.

  • Masking email addresses by consistently replacing the domain name with a generic placeholder (e.g., [email address removed] becomes [email address removed]).

4. Randomized Data Masking:

This technique employs a random algorithm to replace sensitive data with unpredictable values. Each occurrence of the same sensitive data element will be masked differently, enhancing the level of data protection and making reverse engineering significantly more difficult.

• Benefits:

  • Enhanced Security: Randomized masking provides the highest level of data protection, as the unpredictable nature of the masking makes it virtually impossible to reconstruct the original data.

  • Compliance: It aligns with strict data privacy regulations that require irreversible data anonymization.

• Examples:

  • Replacing credit card numbers with randomly generated numbers that follow the same format and length, but with no correlation to the original values.

  • Masking medical records by randomizing patient identifiers and other sensitive information, ensuring complete anonymity.

Choosing the right type of data masking depends on various factors, including the sensitivity of the data, the desired level of security, and the specific use cases for the masked data. A comprehensive data masking solution should offer a range of techniques to cater to diverse data protection needs.

Types of Encryption

Encryption is an essential tool for protecting sensitive information, transforming readable data into an unreadable format that can only be decrypted with the correct key. Various types of encryption cater to different needs, ensuring comprehensive data protection for diverse applications.

Here's a breakdown of the commonly used encryption types:

1. Symmetric Encryption:

This widely employed method utilizes a single secret key for both encryption and decryption. Imagine a single key unlocking a padlock – the same key is used to secure and access the data.

• Benefits:

  • Efficiency: Symmetric encryption offers high processing speed, making it ideal for encrypting large datasets efficiently.

  • Simplicity: The single-key approach simplifies key management compared to asymmetric encryption.

• Examples:

  • Advanced Encryption Standard (AES): This widely adopted algorithm is a robust and efficient symmetric cipher, favored for its security and performance.

  • Triple DES (3DES): This algorithm applies the DES algorithm three times, enhancing security but with lower processing speed compared to AES.

2. Asymmetric Encryption (Public Key Encryption):

This method utilizes a pair of mathematically linked keys – a public key and a private key. The public key is used for encryption, while the private key, kept confidential, is used for decryption. Imagine a two-key lock system – anyone can use the public key to lock (encrypt), but only the holder of the private key can unlock (decrypt).

• Benefits:

  • Secure Key Exchange: Public key encryption allows secure key exchange over public channels. The public key can be freely shared, while the private key remains confidential, eliminating the need to securely transmit shared secret keys.

  • Digital Signatures: Public key cryptography enables digital signatures, verifying the authenticity and integrity of data. The sender signs the data with their private key, and anyone can verify the signature using the corresponding public key.

• Examples:

  • RSA (Rivest–Shamir–Adleman): This widely used algorithm forms the foundation for many public key cryptography applications.

  • DSA (Digital Signature Algorithm): Primarily used for digital signatures and message authentication.

3. Format-Preserving Encryption (FPE):

FPE is a unique type of encryption that scrambles data while preserving its original format. Imagine encrypting a credit card number so it remains a 16-digit number, but the actual digits are scrambled. This allows for seamless integration with existing systems and applications that rely on specific data formats.

• Benefits:

  • Compatibility: FPE eliminates the need for significant system modifications, as the encrypted data maintains the same format as the original data.

  • Functionality: Applications can perform operations on the encrypted data as if it were plaintext, preserving functionality while ensuring confidentiality.

  • Compliance: FPE is often used for encrypting sensitive data like credit card numbers and social security numbers, aligning with data privacy regulations.

4. Database Encryption:

Database encryption focuses on protecting sensitive data stored within databases. This can be achieved through various methods, including:

• Full Database Encryption: Encrypts the entire database, including all tables, columns, and data elements.

• Column-Level Encryption: Selectively encrypts specific sensitive columns within a database table.

• Cell-Level Encryption: Encrypts individual data cells within a database table, offering the most granular level of protection.

• Benefits:

  • Data at Rest Protection: Database encryption safeguards sensitive information stored within databases, minimizing the risk of unauthorized access even if a breach occurs.

  • Compliance Adherence: Helps organizations comply with data privacy regulations that mandate the encryption of sensitive data at rest.

  • Granular Control: Different levels of encryption allow organizations to tailor protection based on the sensitivity of specific data elements.

5. File Encryption:

File encryption protects individual files on storage devices or during transmission. This involves using encryption algorithms to scramble the contents of the file, rendering it unreadable without the decryption key.

• Benefits:

  • Data in Transit Protection: Encrypts files during transfer over networks, safeguarding sensitive information from interception.

  • Data at Rest Protection: Secures files stored on devices like laptops, hard drives, and USB drives, minimizing the risk of unauthorized access if the device is lost or stolen.

  • Compliance Support: Helps organizations comply with regulations requiring the encryption of data in transit and at rest.

Choosing the appropriate encryption type depends on the specific data protection needs and the context in which the data is stored or transmitted. A comprehensive data security strategy often utilizes a combination of these encryption techniques to ensure robust protection across various data storage and transfer scenarios.

The Importance of Data Encryption

Data encryption plays a crucial role in safeguarding sensitive information throughout its lifecycle. Here's a breakdown of its significance:

Confidentiality

Encryption ensures that only authorized individuals with the decryption key can access the original data. This is paramount for protecting sensitive information like financial records, PII, intellectual property, and trade secrets. Even if a data breach occurs, the encrypted data remains unreadable without the key, minimizing the potential for damage.

Data Integrity

Encryption algorithms are designed to detect any unauthorized modifications to the data. If the ciphertext is tampered with, it becomes impossible to decrypt it back to the original plaintext, indicating a potential security breach. This helps organizations maintain the integrity of their data and ensure its accuracy.

Compliance

Numerous data privacy regulations, such as GDPR, HIPAA, and PCI DSS, mandate the encryption of sensitive data at rest and in transit. Implementing robust encryption solutions demonstrates compliance efforts and minimizes the risk of regulatory penalties.

The Importance of Data Masking

Data masking serves as a vital tool for data privacy and compliance, offering distinct advantages:

Reduced Risk of Exposure

Masked data is rendered unidentifiable in its original form, significantly reducing the risk of sensitive information being exposed in case of accidental data breaches or unauthorized access. This minimizes the potential for identity theft, financial fraud, and reputational damage.

Data Usability for Various Purposes

Unlike encrypted data, masked data remains usable for development, testing, and analytics purposes. This enables developers and testers to work with realistic data sets without compromising sensitive information. Data analysts can also extract valuable insights from masked data for research and analytics, while maintaining data privacy compliance.

Compliance Adherence

Data masking helps organizations comply with various data privacy regulations that mandate the protection of sensitive information. By masking sensitive data before sharing it with third-party vendors or storing it in non-production environments, organizations minimize the risk of non-compliance and associated penalties.

Differences Between Data Masking and Encryption

Data masking and encryption are two pivotal strategies for protecting sensitive information, each serving distinct roles. While both aim to secure data from unauthorized access, their methodologies and applications differ significantly.

Purpose

Data masking aims to obscure information in non-production environments, such as development and testing settings, without altering its structure. This allows teams to work with realistic data without risking a leak.

On the other hand, encryption secures data both at rest and in transit, preventing unauthorized access by converting it into an unreadable format that can only be decrypted with the correct key.

Reversibility

Data masking is often permanent, making it ideal for environments where data does not need to be restored to its original form, but must retain its structural integrity.

In contrast, encryption is reversible through decryption, allowing authorized users to regain access to the original information. This is crucial for protecting data while ensuring its availability to legitimate users.

Granularity

Data masking can target specific fields or datasets, allowing for selective protection. This enables organizations to choose which information to mask based on specific conditions or rules.

Encryption, while offering broader application, can also be granular, securing individual files, databases, or fields as needed.

Compliance

Both methods play a role in ensuring compliance with data protection regulations. Data masking helps organizations comply with regulations by anonymizing sensitive information in non-production environments, while encryption helps protect data both at rest and in transit, meeting regulatory requirements in industries such as healthcare and finance.
 

Effective Encryption Solutions & Tool

Organizations face mounting pressure to secure sensitive information from unauthorized access and breaches.

From confidential business data to customer information, securing this data is crucial for maintaining trust, meeting regulatory compliance, and protecting against internal and external threats. A data-centric encryption solution is essential for safeguarding data at rest and in transit.

IRI offers robust encryption solutions designed to address these needs, providing granular and comprehensive options to secure sensitive information across diverse applications.

IRI FieldShield, for example, includes many different encryption options, including format-preserving encryption (FPE), to encrypt database column (or flat-file field) values without changing their format (letters, numbers, case, and punctuation remain). This ensures comprehensive protection while maintaining data realism and referential integrity, making it ideal for database-driven application testing. 

IRI provides support and training to ensure seamless implementation of its encryption solutions. This reassures organizations that their data is protected and their teams are equipped to manage the encryption effectively, minimizing disruptions to workflow and operations.
 

Effective Data Masking Solutions & Tool

Organizations require a secure way to handle sensitive information in non-production environments, allowing teams to develop, test, and work with realistic data without risking a leak. This is especially important in industries where data protection is mandated by law, requiring solutions that balance security and workflow integrity.

IRI offers comprehensive data masking solutions that protect sensitive information in both static and dynamic environments, ensuring security while maintaining data consistency across workflows.

IRI DarkShield, for example, finds and masks (or encrypts) PII and other sensitive information in multiple structured, semi-structured, and unstructured DB and file formats, in both on-premise and cloud silos, via GUI, CLI or API.

DarkShield also supports both deterministic and non-deterministic data masking functions, proven search technologies to discover PII, and the familiarity of Eclipse™ (or OpenAPI) to provide a single, user-friendly interface for running everything at once or in scheduled steps. And, as usual, IRI makes this solution affordable through flexible licensing options.
 

Conclusion

Securing sensitive information is a top priority for modern organizations. Data masking and encryption serve as crucial methods for achieving this goal, each offering unique benefits to address various security challenges.

Implementing comprehensive solutions for data protection ensures organizations can safeguard their information while maintaining workflow integrity and compliance.

See also: https://www.iri.com/blog/data-protection/data-masking-and-data-encryption-are-not-the-same-things.