Pseudonymization vs Anonymization vs Synthetic Data: Understanding Key Data Privacy Techniques
The severe consequences of data breaches for both companies and individuals have led to stringent privacy regulations, and ensuring compliance is critical. Many companies employ pseudonymization and anonymization tools to safeguard personal information and facilitate data sharing, but these have downsides.
While these techniques can strengthen data protection and improve privacy, they can’t guarantee that personal data is no longer identifiable. Besides, pseudonymization and anonymization reduce the statistical quality of data, which can make it less usable.
Our article looks at pseudonymization vs anonymization, describing the differences, and their pros and cons. While anonymization and pseudonymization are both valuable for protecting data, understanding their distinct limitations can help in choosing the right approach. You’ll also see how these techniques compare to synthetic data generation. This will help you understand which approach best suits your business needs.
Table of Contents
What is Pseudonymization?
Pseudonymization (also referred to as pseudo-anonymization) replaces personally identifiable information (PII) and protected health information (PHI) with fake identifiers. For example, this technique replaces a personal identifier like “John Smith” with a pseudonym like “Patient Smith2”.
A key feature of the pseudonymization technique is reversibility. Pseudonymization maintains a mapping table between original and altered datasets, which allows authorized parties to re-identify the information when necessary.
Pseudonymization is accomplished using several methods:
- Data masking (suppression) substitutes the original data with random characters or symbols.
- Tokenization replaces sensitive data elements with non-sensitive equivalents, known as tokens.
- Encryption uses a hash function to turn data into a coded format that can only be deciphered with a specific decryption key.
| Method | Original Data | Processed Data |
|---|---|---|
| Data Masking | John Smith | XXXX XXXX |
| Tokenization | 1234-5678-9012-3456 | Token1234 |
| Encryption | john.smith@example.com | db52d04d81dc9bc2o036db3ed0d83355 |
What Are the Advantages and Disadvantages of Pseudonymized Data?
It’s a misconception that pseudonymization results in anonymized data. Although pseudonymised data can improve privacy, it has several disadvantages.
Advantages of pseudonymization
- Improves privacy: Removing personal identifiers from datasets allows companies to protect sensitive information. It doesn’t prevent the risks of re-identification, though.
- Reversibility: Authorized individuals may use the separately stored mapping table, tokens, and cryptographic keys to restore the original information. Companies can re-identify data for audits, compliance checks, or detailed analytics.
- High utility for testing: Pseudonymized data retains much of its original structure and dependencies, making it useful for business operations and testing.
Disadvantages of pseudonymization
- Not exempt from regulations: Pseudonymized data remains subject to data protection regulations because it is possible to re-identify individuals using additional information. Businesses must still comply with CCPA, HIPAA, and GDPR requirements.
- Security risks: The mapping table and cryptographic keys must be stored in a secure location because unauthorized access can lead to data breaches.
- Accuracy reduction: Pseudonymized data may not fully capture the nuances of real-world data, resulting in lower accuracy and reliability in analysis.
Despite providing a degree of protection, pseudonymized data still poses several privacy and security risks and may not be suitable for advanced analytics. Companies must invest in reliable pseudonymized methods that balance privacy with data utility.
What is Anonymization?
Anonymization means altering or removing sensitive information from datasets to ensure that individuals aren’t identifiable. Unlike pseudonymization, which replaces personal identifiers with pseudonyms, anonymization eliminates all traces of PII. It’s nearly impossible to identify an individual without additional information or context. By creating anonymous data, businesses can reduce the risk of data breaches and ensure compliance with privacy regulations.
Popular methods of anonymization include:
- Data generalization (aggregation) groups similar data together and diminishes detail.
- Data minimization (perturbation) slightly changes the information and adds noise to prevent exact identification.
- Data swapping rearranges attributes in the values to make the sensitive information unrecognizable.
- Randomization alters values with random strings of characters and numbers (mock data).
| Method | Original Data | Processed Data |
|---|---|---|
| Generalization (aggregation) | 27 years old | Between 25 and 30 years old |
| Minimization (perturbation) | 202 Maple St. | 204 Maple St. |
| Swapping | John Smith, 35 years old | Jane Jones, 40 years old |
| Randomization | 555-1234 | 789-5678 |
What Are the Advantages and Disadvantages of Anonymized Data?
Manipulating a dataset with classic anonymization techniques results in several advantages and disadvantages.
Advantages of anonymization
- Compliant data: Anonymized datasets don’t contain anything that’s considered personal data. So, they aren’t subject to general data protection regulations, which allows companies to focus on leveraging the data for insights and decision-making.
- Facilitates data sharing: Businesses can share this anonymous data with researchers, partners, and stakeholders while complying with data protection laws.
Disadvantages of anonymization
- Decreased data accuracy: When anonymizing data, you can obscure meaningful patterns and contextual details. This can severely reduce the usability of this data for research, software testing, or data-driven decision-making.
- Minor risk of re-identification: While anonymization (vs pseudonymization) has a higher degree of privacy built-in, it’s still possible to re-identify data when combined with other data sources with the use of advanced computational tools.
- Irreversibility can limit use cases: After being rendered anonymous, the personal data cannot be reverted to its original form, which can be problematic if you want to re-identify data for audits or other statistical purposes.
Companies must invest in sophisticated algorithms, data controllers, and differential privacy frameworks to maintain the necessary privacy levels and data usability. An alternative is to create fully artificial data.
How is Synthetic Data Different from Pseudonymization?
Synthetic data is artificially generated based on real data. Since it’s created from scratch, it doesn’t contain any PII or PHI, making the generated datasets fully private and exempting them from data privacy regulations, and helps protect personal information. Additionally, synthetic data generation tools use AI and machine learning algorithms that imitate the statistical properties of real information.
Based on the generation method, synthetic data can be divided into several categories:
- Fully AI-generated synthetic data that mimics the statistical patterns, relationships, and characteristics of real-world data using AI algorithms. Trained on real-world data, these AI models generate new data that closely replicates the original data’s features, allowing for advanced analytics. This “synthetic data twin” can be used as if it were real-world data.
- Synthetic mock data that substitutes sensitive PII, PHI, and other identifiers with mockers that follow business logic and patterns. At Syntho, we call this approach a smart de-identification process, supported by over 150 mockers in various languages and alphabets, including default mockers (e.g., first name, last name, phone numbers) and advanced mockers to generate data that adheres to your business rules.
- Rule-based synthetic data that follows predefined business rules and constraints to generate artificial data. You can use this approach to create data from scratch when real data is limited, enrich existing datasets with additional rows and columns, ensure data quality through cleansing, and protect privacy by avoiding using real personal data.
Unlike anonymization and pseudonymization, synthetic data generation learns from real data to create realistic datasets. The AI model analyzes the original dataset to identify critical patterns and relationships that make the data useful for advanced analytics. After the processing of personal data, the tool identifies direct or indirect identifiers. The end result creates new data that doesn’t include specific data subjects.
What Are the Advantages and Disadvantages of Synthetic Data?
Advantages of synthetic data generation
- Complete privacy: Compared to pseudonymized data, synthetic data doesn’t contain original data with PII. This makes it truly anonymous information that complies with data privacy laws and eliminates potential damage from data breaches.
- High statistical accuracy: Synthetic data mimics the original data’s structure, making it useful for advanced modeling and analysis. Organizations can train AI models, conduct in-depth clinical research, and perform research without compromising on accuracy.
- Easy access to data: Advanced synthetic data platforms enable companies to quickly produce compliant datasets of varying sizes and complexities tailored to specific needs.
- Data compatibility: Synthetic data can be created in various formats supported by different systems, preventing compatibility issues. This ensures seamless integration into existing workflows and tools, whether the data is in a textual, tabular, or graphical format.
Disadvantages of synthetic data generation
- Requires significant computational resources: Synthetic data generation methods, especially those involving complex encryption or advanced modeling, demand significant computational power. This can be a limitation for DevOps and Quality Assurance (QA) teams needing quick data access for testing and development.
- Need for expertise: High-quality synthetic data generation requires advanced algorithms and experience, necessitating investment in development and specialized skills.
To avoid these constraints, companies can purchase a ready-made synthetic data generation platform. A reputable provider will help integrate the technology into their workflow, provide the necessary toolset, and train their employees.
Should You Use Synthetic Data Instead of Real Data?
Synthetic data allows companies to create, use, share, and sell high-quality testing and analytical data without security or privacy compliance risks.
When dealing with real data, you have to comply with several data privacy and security requirements. This has several implications for the usability of your datasets. For example, you can’t freely use data among your departments or share it with other companies.
Real data can be scarce, especially for rare events or conditions. Synthetic data generation platforms allow your employees to create an anonymized dataset for any use case on the go. This helps you make your training data more inclusive and, consequently, less prone to biases.
Due to high statistical accuracy, your employees can produce data to develop and refine AI models without the risk of exposing a natural person or business entity. Synthetic datasets are often used to share data with other businesses without the red tape of data privacy regulations. Some companies even create marketplaces to sell high-quality artificial data.
Finally, advanced synthetic data generation solutions include validation tools that help gauge the statistical accuracy of the synthetic data vs anonymized or pseudonymized data.
| Value for analysis | Privacy risk | |
|---|---|---|
| Synthetic data | High | Low |
| Real (personal) data | High | High |
| Anonymization | Low-Medium | Medium-High |
| Pseudonymization | Medium-High | Medium |
Conclusion: Data Anonymization vs Pseudonymization vs Synthetic Data
Anonymization and pseudonymization come with various trade-offs. If you pseudonymize data, it can no longer be attributed to specific data subjects, but it doesn’t exclude the data from privacy regulations entirely. Anonymization makes your datasets compliant but can significantly reduce the data utility.
Synthetic data generation combines the best of both methods without their shortcomings. Our smart synthetic data generation platform produces compliant data that mimics the qualities of the original information.
Do you want to learn more? Feel free to read about the practical use cases of synthetic data and its benefits for privacy-focused sectors like healthcare. Better yet, contact us for a consultation or to schedule a demo.
About the author
Customer Service Engineer & Data Scientist
Shahin Huseyngulu has a strong academic foundation in Computer Science and Data Science and is an experienced Customer Service Engineer and Data Scientist. Shahin has held key roles in customer service, cloud solutions, and machine learning research, showcasing expertise in Python, SQL, and data analytics. Currently, Shahin excels as a Customer Service Engineer at Syntho, building and optimizing customer service operations while bringing a unique blend of technical and customer service skills to drive innovation and customer satisfaction in the tech industry.
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