Encryption, Tokenization, Masking, and Redaction: Choosing the Right Approach (2024)

What’s the best way to protect sensitive data?

The answer, of course, is “it depends.” Organizations have too many different types of sensitive information, and too many ways to store and share it, to allow for a one-size-fits-all approach. Each of the common methods of protecting data—encryption, tokenization, masking, and redaction—might be the right solution for a given use case.

Encryption

Typical uses:Secure data exchange; protecting data at rest; structured and unstructured data

Encryption is the strongest and most commonly-used method for protecting sensitive data. When properly implemented, encryption cannot be defeated by any known technology.

Encryption uses complex algorithms to convert the original data (plaintext) into unreadable blocks of text (ciphertext) that can’t be converted back into readable form without the appropriate decryption key.

Encryption can be implemented in many different ways, each suited to different use cases. Network encryption protects data as it travels, leaving data in the clear on either end of a transmission. Transparent encryptionprotects data at rest, decrypting the data before it’s accessed by authorized users.Persistent encryption protects data regardless of where it’s stored or copied, providing maximum protection against inappropriate use. Format preserving encryption protects data while maintaining the original formatting and length of the data.

Tokenization

Typical uses:Payment processing systems; structured data

Tokenization, like encryption, is a reversible process that replaces sensitive data with data that can’t be used by unauthorized parties. While encryption uses algorithms to generate ciphertext from plaintext, tokenization replaces the original data with randomly-generated characters in the same format (token values). Relationships between the original values and token values are stored on a token server. When a user or application needs the correct data, the tokenization system looks up the token value and retrieves the original value.

Tokenization is often used to protect credit card numbers or other sensitive information in payment processing systems, customer service databases, and other structured data environments. However, length-and-format-preserving encryption can address the same use cases, often with less complexity.

Masking

Typical uses:Test environments; structured data

Masking is essentially permanent tokenization. Sensitive information is replaced by random characters in the same format as the original data, but without a mechanism for retrieving the original values. This is a common practice in test environments, which require realistic-looking data but cannot be populated with actual customer or employee data.

Masking can also be used to control access to sensitive data based on entitlements. This approach, known as dynamic data masking, allows authorized users and applications to retrieve unmasked data from a database, while providing masked data to users who are not authorized to view the sensitive information.

Redaction

Typical uses:Unstructured data; legacy data

Redaction is the permanent removal of sensitive data—the digital equivalent of “blacking out” text in printed material. Redaction can be accomplished by simply deleting characters from a file or database record, or by replacing characters with asterisks or other placeholders.

Automated data redaction is an effective method of eliminating sensitive data from documents, spreadsheets, and other files, without altering the remaining file contents. Organizations often adopt this approach to prevent the spread of sensitive information that has been extracted from a database and saved on file servers, laptops, or desktops.

Choosing a Solution

For use cases that involve sharing sensitive information between users, teams, or organizations, persistent encryption is the most effective option. No other technology provides adequate protection against misuse, while allowing access by authorized parties. A detailed strategy forencryption key management, including key creation, storage, exchange, and rotation, is essential for maintaining the security of an encryption system. With no endpoint software required, PK Encryption quickly secures files and data without application changes, additional infrastructure, or professional services. And it accomplishes all this without disrupting existing workflows.

For other use cases, the choice between encryption, tokenization, masking, and redaction should be based on your organization’s data profile and compliance goals. In some cases, a combination of technologies may be the best approach. Solutions such as PK Masking can be added to PK Encryption to mask or redact sensitive information, protecting privacy while maximizing data value.

PKWARE can help can help your organization design and implement a data security strategy that automatically protects data at the moment of creation, and keeps it safe no matter where files are copied or shared. Find out how solutions such as PK Encryption and PK Masking, part of the PK Protect suite, can help you meet your data protection and compliance goals. Get a free personalized demo now.

Encryption, Tokenization, Masking, and Redaction: Choosing the Right Approach (2024)

FAQs

What is tokenization and masking? ›

Data masking is used to protect sensitive data while allowing the use of realistic test or demo data, while tokenization is used to protect sensitive data while allowing authorized users to access and process the tokenized data, for example, for use in analytics.

What is the difference between redaction and tokenization? ›

Tokenization, redaction, and masking are three techniques used for PHI deidentification. Tokenization replaces sensitive PHI with unique identifiers, redaction removes or obscures individual identifiers, and masking conceals a portion of the data element.

What is tokenization and encryption? ›

Tokenization focuses on replacing data with unrelated tokens, minimizing the exposure of sensitive information, and simplifying compliance with data protection regulations. Encryption, on the other hand, secures data by converting it into an unreadable format, necessitating a decryption key for access.

What is the difference between redaction and encryption? ›

Redaction is less secure because it can leave traces of the underlying data intact and recoverable for malicious actor use. Encryption offers a higher level of security by altering the data itself. Even if intercepted, the encrypted data remains secure against cyber-attacks, rendered unreadable and useless.

What is the difference between encryption and masking? ›

Masking is clearly more secure than encryption but it renders data useless. Masked data has no value for anyone who intercepts or steals it. This information cannot be used for anything other than to run tests on software in a development state. Hackers don't want or care about masked development data.

What is the difference between redact and mask? ›

While data redaction is the process of removing certain pieces of sensitive or personally identifiable information, data masking is a process in which sensitive and authentic information is replaced with inauthentic information that has the same structure.

What is an example of tokenization? ›

Payment Tokenization Example

When a merchant processes the credit card of a customer, the PAN is substituted with a token. 1234-4321-8765-5678 is replaced with, for example, 6f7%gf38hfUa. The merchant can apply the token ID to retain records of the customer, for example, 6f7%gf38hfUa is connected to John Smith.

What is the purpose of tokenization? ›

What is the Purpose of Tokenization? The purpose of tokenization is to protect sensitive data while preserving its business utility. This differs from encryption, where sensitive data is modified and stored with methods that do not allow its continued use for business purposes.

How is data masking done? ›

Dynamic data masking works as follows: All users communicate with the database via a proxy server. When users request to read data, the database proxy applies masking rules based on user roles, privileges, or access permissions. Authorized users receive the original data, while unauthorized users receive masked data.

Can tokenization be hacked? ›

The token and number itself could be hacked, but it would take years of attempts to be successful because of the encryption methods. While security tokens are generally considered to be a secure method of authentication, they are not completely immune to attacks.

What is tokenization for dummies? ›

Tokenization is the process of creating a digital representation of a real thing. Tokenization can also be used to protect sensitive data or to efficiently process large amounts of data.

What are the pros and cons of tokenization? ›

The pros of tokenization—increased accessibility, liquidity, and efficiency—should be weighed against the cons—regulatory uncertainty, technological complexity, and security risks.

What are the three 3 different encryption methods? ›

There are different types of encryption techniques, but the following three are the most common and widely used: Symmetric Encryption, Asymmetric Encryption, and Hashing.

Why is redaction used? ›

Key Takeaways. Redacted documents are edited to conceal or remove confidential information before disclosure or publication. The redaction process is a common practice in legal documents. Redacting personal data in documents is important to avoid identity theft.

What does "to redact" mean? ›

: to obscure or remove (text) from a document prior to publication or release.

What is tokenization in simple terms? ›

Tokenization is the process of exchanging sensitive data for nonsensitive data called “tokens” that can be used in a database or internal system without bringing it into scope.

What is masking in data processing? ›

Data masking creates fake versions of an organization's data by changing confidential information. Various techniques are used to create realistic and structurally similar changes. Once data is masked, you can't reverse engineer or track back to the original data values without access to the original dataset.

What is masked token? ›

The use of mask token ([MASK]) in H2O.ai's machine learning pipelines can enhance the performance of language modeling, text prediction, and various NLP tasks. It allows users to develop models that can generate accurate and contextually meaningful predictions in their specific business applications.

What is the tokenization method? ›

Tokenization is used in natural language processing to split paragraphs and sentences into smaller units that can be more easily assigned meaning. The first step of the NLP process is gathering the data (a sentence) and breaking it into understandable parts (words).

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