Pseudonymized data replaces PII with artificial identifiers that deter unauthorized access or disclosure. Examine the pros and cons of this technique here.
Table of Contents
Protecting User Privacy with Pseudonymized Data
What is Pseudonymized Data?
Pseudonymized Data vs Anonymized Data
Methods for the Delivery of Pseudonymized Data: Pros and Cons
Benefits of Pseudonymized Data
Challenges and Limitations of Pseudonymized Data
Pseudonymized Data Based on Business Entities
Pseudonymized data is data that has been de-identified by replacing direct identifiers – such as names, addresses, or Social Security Numbers – with fictional code or symbols, called pseudonyms.
Enterprises employ pseudonymization – a technique commonly found in data tokenization tools – to protect individuals’ privacy and sensitive information, support compliance with data privacy regulations, and reduce the potential impact of a breach. At the same time, data that has been pseudonymized may continue to be used for analysis and other purposes. Pseudonymization is typically used to protect credit card payment information.
In this article, we’ll provide a detailed overview of pseudonymized data, explain its advantages and disadvantages, and introduce a business entity approach to data pseudonymization.
Data pseudonymization is a common data anonymization technique. It conceals sensitive data by replacing PII (Personal Identifiable Information) with artificial identifiers to reduce the risk of exposure resulting from unauthorized access or disclosure. Pseudonymized datasets can still be used for legitimate purposes, such as business analytics, marketing, and sharing data with third parties. Other data anonymization techniques include data masking, synthetic data generation, and tokenization.
Unlike other data protection methods, such as data masking tools, pseudonymization is typically reversible. Since sensitive data can be re-identified via a controlled re-identification process, pseudonymization is often used in combination with other data protection techniques, such as data masking vs encryption.
While pseudonymized data and anonymized data both serve to reduce data identifiability, they have significant differences. In a comparison between pseudonymization vs anonymization, the key difference is that pseudonymized data can be recovered, while anonymized data can’t be re-identified.
While data pseudonymization tools obscure the link between data and the individuals it corresponds to, data anonymization tools nullify this link. For this reason, data pseudonymization, alone, is usually insufficient for complying with data privacy laws like GDPR, CCPA, and HIPAA.
However, in instances where total anonymization isn’t necessary, pseudonymization is a simpler way to obfuscate data, while preserving the integrity of the identification chain.
Here’s an overview of 5 most common data pseudonymization methods, along with their relevant advantages and disadvantages.
Counter
In this approach, identifiers are substituted by a number chosen by a monotonic counter. For example, first a seed 𝑠 is set to 0, and then it is incremented each time a new pseudonym is needed. (Note that the values should never repeat in order to prevent ambiguity).
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Random Number Generator (RNG)
Although similar to the counter, the RNG mechanism produces values that have an equal probability of being selected from the total population of possibilities (rather than producing them based on increments).
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Cryptographic Hash Function (CHF)
A hash function takes a data input and produces a fixed-length output, known as a hash value, or digest. To pseudonymize data using a cryptographic hash function, the original data is first hashed using the function. The resulting hash value is then used in place of the original data for certain purposes, such as analysis or storage.
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Outputs can’t be mapped back to inputs, providing additional security Vulnerable to brute force and dictionary attacks |
Message Authentication Code (MAC)
MAC is similar to CHF, above, except that it uses a secret key to generate pseudonyms. Without the key, it’s impossible to map pseudonyms back to identifiers.
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Encryption
Encryption can be used to pseudonymize data by applying a mathematical algorithm to the original data, transforming it into ciphertext. The ciphertext can only be decrypted back into its original form with a decryption key.
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Pseudonymized data offers enterprises many advantages, including:
Support for privacy compliance
Although it’s not sufficient on its own, pseudonymization can support an enterprise’s efforts to comply with data protection laws by reducing the risk of unauthorized access to sensitive data.
Lower risk of data breaches
In the event of a data breach, pseudonymization makes it more difficult for attackers to identify and access sensitive data.
Preserved data utility
Pseudonymized data can remain functional for a variety of use cases, including analytics, customer engagement campaigns, research, and more, while protecting the privacy of individuals.
Increased customer trust
Customers today expect enterprises to make great efforts to protect their privacy. Data pseudonymization helps enterprises demonstrate their commitment to protecting customer privacy and earn their trust.
Easier data sharing with less risk
Pseudonymization protects PII when data is in transit or used in third-party systems, making it easier and less risky to share data across organizations.
Reduced cost of data protection
Pseudonymized data can help offset the cost of data protection by eliminating or reducing the need for certain physical security measures.
Improved data governance
Enterprises can use pseudonymization in conjunction with their data governance tools to gain greater control over data access.
In addition to its benefits, here’s an overview of challenges and limitations associated with pseudonymized data:
Risk of re-identification
With pseudonymized data, the risk of re-identification of anonymized data always exists. Determined attackers, who combine pseudonymized data with other available information (such as MAC or encryption keys), can potentially identify original data.
Diminished data quality
Pseudonymization can sometimes lead to a loss in data quality, making it difficult for enterprises to conduct analytics accurately.
Cost and complexity
For some organizations, implementing pseudonymization requires additional expertise and resources. The cost and complexity of pseudonymizing data rises as the size of datasets increases.
One of the most advanced and robust methods for data pseudonymization is based on the business entity approach to data masking challenges. A business entity approach 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, device, or order).
The data for every instance of a business entity is managed in an individually encrypted Micro-Database™, which is either stored, or cached in memory – one for each entity.
When entity-based data pseudonymization is based on intelligent business rules, companies can enhance compliance efforts, ensure data privacy, and reduce data protection costs – without compromising on data utility, productivity, or speed.