How do you balance data utility and data privacy when applying data masking and obfuscation techniques? (2024)

FAQs

What is data masking and obfuscation techniques? ›

Data masking, or data anonymization, is a data obfuscation technique whereby sensitive data like encryption keys, personal information or authentication tokens and credentials are redacted from logged messages. Data masking changes the value of data while using the same format for the masked data.

Three of the most common techniques used to obfuscate data are encryption, tokenization, and data masking. Encryption, tokenization, and data masking work in different ways. Encryption and tokenization are reversible in that the original values can be derived from the obfuscated data.

One of the key considerations in data masking is ensuring that the process is irreversible. This means once sensitive data has been masked, it should not be possible to reconstruct or retrieve the original data from the masked version.

Determine the Project Scope

In order to effectively perform data masking, companies should know what information needs to be protected, who is authorized to see it, which applications use the data, and where it resides, both in production and non-production domains.

Data obfuscation is the process of replacing sensitive information with data that looks like real production information, making it useless to malicious actors.

Data masking is the process of hiding data by modifying its original letters and numbers. Due to regulatory and privacy requirements, organizations must protect the sensitive data they collect about their customers and operations.

In simple terms, data manipulation is the moving around and preparing of data before any analysis takes place. Meanwhile, the three different types include manual, semi automated and fully automated.

The primary goal of data masking is to ensure that the masked data resembles the real data and can be used for development, testing, or analysis without exposing sensitive information. This usually involves hiding a certain subset of sensitive data.

Replacing personally-identifying details and names with other symbols and characters. Moving details around or randomizing sensitive data like names or account numbers. Scrambling the data, substituting parts of it for other parts from the same dataset. Deleting or “nulling out” sensitive values within data records.

What is the primary goal of data masking? ›

Data masking is a method of creating a structurally similar but inauthentic version of an organization's data that can be used for purposes such as software testing and user training. The purpose is to protect the actual data while having a functional substitute for occasions when the real data is not required.

To test adequately, realistic data is essential, but real data is notorious for creating runs considerable data security risks. Data masking eliminates the risk of personal data exposure in compliance with data privacy regulations.

Encryption. Encryption is the process of converting data into a code that can only be read by authorized parties. This technology is a critical component of data protection, as it can help prevent data theft or unauthorized access.

The most common types of data that need masking are: Personally Identifiable Information (PII) such as names, passport, social security, and telephone numbers. Protected Health Information (PHI) about an individual's health status, care, or payment data.

Data masking is a method of creating structurally similar but non-realistic versions of sensitive data. Masked data is useful for many purposes, including software testing, user training, and machine learning datasets.

Within the illegal drug trade, obfuscation is commonly used in communication to hide the occurrence of drug trafficking. A common spoken example is "420", used as a code word for cannabis, a drug which, despite some recent prominent decriminalization changes, remains illegal in most places.

Unlike data anonymization, data masking is a reversible process. This means that the original data can be restored when needed. Data masking is commonly used in software development and testing environments where real data is required, but exposing sensitive information could lead to security risks.

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.

Encryption can detect if the encrypted data has been altered, as the decryption process will fail if the ciphertext has been tampered with. Obfuscation does not provide tamper protection, as the code remains in a readable form and can be easily modified by an attacker.