TABLE OF CONTENT
- Why care about data anonymization tools?
- Data anonymization tools: What are they anyway?
- How do data anonymization tools work?
- Legacy data anonymization tools
- The next generation of data anonymization tools
- Data anonymization tools and their use cases
- The best and the worst data anonymization tools
Why should you care about data anonymization tools?
Data anonymization tools can be your best friends or your data quality’s worst enemies. Sometimes both. Anonymizing data is never easy, and it gets trickier when:
- You collect more data,
- Your datasets become more complex,
- Your adversaries come up with new types of privacy attacks,
- You remove PII from your data, thinking it will provide privacy protection,
- You add too much noise to the data and destroy the intellingence.
You try to do your best and use data anonymization tools on a daily basis. You have removed all sensitive information, masked the rest, and randomized for good measure. So, your data is safe now. Right?
As the Austrians—Arnold Schwarzenegger included—say: Schmäh! Which roughly translates as bullshit. Why do so many data anonymization efforts end up being Schmäh?
Data anonymization tools: What are they anyway?
Data anonymization tools conveniently automate the process of data anonymization with the goal of making sure that no individual included in the data can be re-identified. The most ancient of data anonymization tools, namely aggregation and the now obsolete rounding, was born in the 1950s. The concept of adding noise to data as a way to protect anonymity entered the picture in the 1970s. We have come a long way since then. Privacy-enhancing technologies were born in the 90s and have been evolving since, offering better, safer, and more data-friendly data anonymization tools.
Data anonymization tools must constantly evolve since attacks are also getting more and more sophisticated. Today, new types of privacy attacks using the power of AI, can reidentify individuals in datasets that are thought of as anonymous. Data privacy is a constantly shifting field with lots of moving targets and constant pressure to innovate.
Data anonymization tools: How do they work?
Although a myriad of data anonymization tools exist, we can differentiate between two groups of data anonymization tools based on how they approach privacy in principle. Legacy data anonymization tools work by removing personally identifiable information, or so-called PII. Traditionally, this means unique identifiers, such as social security numbers, credit card numbers, and other kinds of ID numbers.
The trouble with these types of data anonymization tools is that no matter how much of the data is removed, a 1:1 relationship between the data subject and the data points remains. With the advances of AI-based reidentification attacks, it’s getting increasingly easier to find this 1:1 relationship, even in the absence of obvious PII pointers. Our behavior—essentially a series of events—is almost like a fingerprint. An attacker doesn’t need to know my name or social security number if there are other behavior-based identifiers that are unique to me, such as my purchase history or location history. As a result, state of the art data anonymization tools are needed to anonymize behavioral data.
Which data anonymization tools can be considered legacy?
Legacy data anonymization tools are often associated with manual, rule-based systems, whereas modern data privacy solutions incorporate machine learning and AI to achieve more dynamic and effective results. Rule-based systems are not only easy to break but are difficult to maintain when applied to large amounts of data across multiple platforms, serving different data consumers with different requirements for data utility.
1. What is data masking?
Data masking is one of the most frequently used data anonymization tools across industries. It works by replacing the original data with asterisks or another placeholder. Data masking can reduce the value or utility of the data, especially if it's too aggressive. The data might not retain the same distribution or characteristics as the original, making it less useful for testing or analysis.
The process of data masking can be complex, especially in environments with large and diverse datasets. The masking should be consistent across all records to ensure that the data remains meaningful. Maintaining data integrity is no small feat either. The masked data should adhere to the same validation rules, constraints, and formats as the original dataset. This can be challenging to achieve, especially in databases with many interdependencies. Over time, as systems evolve and new data is added or structures change, ensuring consistent and accurate data masking can become challenging.
Traditional data masking tools are often designed for relational databases. Masking data in non-relational databases, NoSQL databases, or unstructured data sources can be difficult. Simply removing PII from data using Python, for example, still has its place, but the resulting data should not be considered anonymized by any stretch of the imagination.
2. What is pseudonymization?
Pseudonymization replaces private identifiers with fake identifiers, or pseudonyms. While the data can still be matched with its source when one has the right key, it can't be matched without it. The 1:1 relationship remains and can be recovered not only by accessing the key but also by linking different datasets. The risk of reversibility is always high, and as a result, pseudonymization should only be used when it’s absolutely necessary to reidentify data subjects at a certain point in time.
The pseudonyms typically need a key for the transformation process. Managing, storing, and protecting this key is critical. If the key is lost, data might become irretrievable. If it's compromised, the pseudonymization can be reversed.
What’s more, under GDPR, pseudonymized data is still considered personal data, meaning that data protection obligations continue to apply. This is great, considering that as data analysis and re-identification techniques evolve, what may be considered sufficiently pseudonymized today might be vulnerable in the future.
Overall, while pseudonymization might be the most widely used data anonymization tool, it should only be used as a stand-alone tool when absolutely necessary. Pseudonymization is not anonymization and pseudonymized data should never be considered anonymized.
3. What is generalization and aggregation?
This method reduces the granularity of the data. For instance, instead of displaying an exact age of 27, the data might be generalized to an age range, like 20-30. Generalization causes a very significant loss of data utility by decreasing data granularity. Over-generalizing can render data almost useless, while under-generalizing might not provide sufficient privacy.
You also have to consider the risk of residual disclosure. Generalized data sets might contain enough information to infer about individuals, especially when combined with other data sources.
Generalization can be useful, not necessarily as a data anonymization tool, but to provide a clearer overview or summary of large datasets.
4. What is data swapping or perturbation?
Original data values are replaced with values from other records. The privacy-utility trade-off strikes again: perturbing data can lead to a loss of information, which can affect the accuracy and reliability of analyses performed on the perturbed data. Protecting against re-identification while maintaining data utility is challenging. Finding the appropriate perturbation methods that suit the specific data and use case is not always straightforward.
5. What is randomization?
Randomization is a set of legacy data anonymization tools that change the data to make it less connected to a person. When the data is made less certain, it becomes hard to figure out which person it belongs to.
Some data types, such as geospatial or temporal data, can be challenging to randomize effectively while maintaining data utility. Preserving spatial or temporal relationships in the data can be complex.
Selecting the right algorithm to do the job is also challenging since each data type and use case could call for a different approach. Choosing the wrong tool can have serious consequences downstream, resulting in inadequate privacy protection or excessive data distortion.
Data consumers could be unaware of the effect randomization had on the data and might end up with false conclusions. On the bright side, randomization techniques are relatively straightforward to implement, making them accessible to a wide range of organizations and data professionals.
6. What is data redaction?
Data redaction is similar to data masking, but in the case of this data anonymization tool, entire data values or sections are removed or obscured. Deleting PII is easy to do. However, it’s a sure-fire way to encounter a privacy disaster down the line. It’s also devastating for data utility since critical elements or crucial contextual information could be removed from the data.
Redacted data may introduce inconsistencies or gaps in the dataset, potentially affecting data integrity. Redacting sensitive information can result in a smaller dataset. This could impact statistical analyses and models that rely on a certain volume of data for accuracy.
7. What is tokenization?
This technique replaces sensitive data with unique symbols or tokens. The original data is stored securely in a separate database. Managing different versions of tokenized data is relatively straightforward, and tracking changes can be more manageable than some other anonymization methods. Maintaining tokenization rules that are adequate for privacy protection is harder, especially in datasets with interconnected and multifaceted information. Maintaining a mapping between tokens and original data can require additional storage, particularly for large datasets with many unique tokens.
Next-generation data anonymization tools
The next-generation data anonymization tools, or so-called privacy-enhancing technologies take an entirely different, more use-case-centered approach to data anonymization and privacy protection. There are two groups of privacy-enhancing technologies: cryptographic PETs and statistical PETs.
1. Homomorphic encryption
The first group of modern data anonymization tools works by encrypting data in a way that allows for computational operations on encrypted data. Homomorphic encryption is the prime example of encryption-based data anonymization tools. The downside of these technologies is that they are computationally very intensive and, as such, not widely available and cumbersome to use. As the price of computing power decreases and capacity increases, these technologies are set to become more popular and easier to access.
2. Federated learning
The second group of PETs works by extracting statistical information from the data and leaving sensitive information behind. Examples would include privacy-protecting machine learning architectures, like federated learning. Federated learning is a fairly complicated approache enabling machine learning models to be trained on distributed datasets. Federated learning is commonly used in applications that involve mobile devices, such as smartphones and IoT devices.
For example, predictive text suggestions on smartphones can be improved without sending individual typing data to a central server. In the energy sector, federated learning helps optimize energy consumption and distribution without revealing specific consumption patterns of individual users or entities. However, these federated systems require the participation of all players, which is near-impossible to achieve if the different parts of the system belong to different operators. Simply put, Google can pull it off, while your average corporation would find it difficult.
3. Synthetic data generation
A more readily available approach is an AI-powered data anonymization tool: synthetic data generation. Synthetic data generation extracts the distributions, statistical properties, and correlations of datasets and generates entirely new, synthetic versions of said datasets, where all individual data points are synthetic. The synthetic data points look realistic and, on a group level, behave like the original. As a data anonymization tool, reliable synthetic data generators produce synthetic data that is representative, scalable, and suitable for advanced use cases, such as AI and machine learning development, analytics, and research collaborations.
4. Secure multiparty computation (SMPC)
Secure Multiparty Computation (SMPC), in simple terms, is a cryptographic technique that allows multiple parties to jointly compute a function over their private inputs while keeping those inputs confidential. It enables these parties to collaborate and obtain results without revealing sensitive information to each other.
While it's a powerful tool for privacy-preserving computations but comes with its set of implementation challenges, particularly in terms of complexity, efficiency, and security considerations. It requires expertise and careful planning to ensure that it is applied effectively and securely in practical applications.
Data anonymization tools and their use cases
Data anonymization tools encompass a diverse set of approaches, each with its own strengths and limitations. In this comprehensive guide, we explore eleven key data anonymization tools and strategies, ranging from legacy methods like data masking and pseudonymization to cutting-edge approaches such as federated learning and synthetic data generation. Whether you're a data scientist or privacy officer, you will find this bullshit-free table listing their advantages, disadvantages, and common use cases very helpful.
|#||Data Anonymization Tool||Description||Advantages||Disadvantages||Use Cases|
|1||Data Masking||Masks or disguises sensitive data by replacing characters with symbols or placeholders.||- Simplicity of implementation.|
- Preservation of data structure.
- Suitable for text-based data.
|- Limited protection against inference attacks.|
- Potential confusion in data analysis due to masking.
- May not preserve data relationships.
|- Anonymizing email addresses in communication logs.|
- Concealing rare names in datasets.
- Masking sensitive words in text documents.
|2||Pseudonymization||Replaces sensitive data with pseudonyms or aliases.||- Preservation of data structure.|
- Data utility is generally preserved.
- Fine-grained control over pseudonymization rules.
|- Risk of re-identification if pseudonyms are not well-protected.|
- Requires secure management of pseudonym mappings.
- Additional storage overhead for pseudonym mapping.
|- Protecting patient identities in medical research.|
- Anonymizing customer names in marketing databases.
- Securing employee IDs in HR records.
|3||Generalization/Aggregation||Aggregates or generalizes data to reduce granularity.||- Simple implementation.|
- Data utility preservation for certain analyses.
|- Loss of fine-grained detail in the data.|
- Risk of data distortion that affects analysis outcomes.
- Challenging to determine appropriate levels of generalization.
|- Anonymizing age groups in demographic data.|
- Concealing income brackets in economic research.
|4||Data Swapping/Perturbation||Swaps or perturbs data values between records to break the link between individuals and their data.||- Flexibility in choosing perturbation methods.|
- Potential for fine-grained control.
- Scalability for large datasets.
|- Privacy-utility trade-off can be challenging to balance.|
- Risk of introducing bias in analyses.
- Selection of appropriate perturbation methods is crucial.
- Online user behavior analysis.
|5||Randomization||Introduces randomness into the data to protect data subjects.||- Potential for data utility preservation.|
- Flexibility in applying to various data types.
- Reproducibility of results when using defined algorithms and seeds.
|- Privacy-utility trade-off can be challenging to balance.|
- Risk of introducing bias in analyses.
- Selection of appropriate randomization methods is hard.
|- Anonymizing survey responses in social science research.|
- Online user behavior analysis.
|6||Data Redaction||Removes or obscures specific parts of the dataset containing sensitive information.||- Simplicity of implementation.|
- Version control is relatively straightforward.
|- Loss of data utility, potentially significant.|
- Risk of removing contextual information.
- Data integrity challenges.
|- Concealing personal information in legal documents.|
- Hiding confidential details in financial statements.
- Masking private data in text documents.
|7||Tokenization||Replaces sensitive data with unique tokens or references.||- Preservation of data structure.|
- Data utility is generally preserved.
- Fine-grained control over redaction.
|- Risk of inference if tokenization rules are not well-defined.|
- Requires secure management of token mappings.
- Additional storage overhead for token mapping.
|- Protecting credit card numbers in payment processing.|
- Anonymizing patient IDs in healthcare records.
- Securing social security numbers in HR databases.
|8||Homomorphic Encryption||Encrypts data in such a way that computations can be performed on the encrypted data without decrypting it, preserving privacy.||- Strong privacy protection for computations on encrypted data.|
- Supports secure data processing in untrusted environments.
- Cryptographically provable privacy guarantees.
|- Complexity of encryption and decryption operations.|
- Performance overhead for cryptographic operations.
- May require specialized libraries and expertise.
|- Basic data analytics in cloud computing environments.|
- Privacy-preserving machine learning on sensitive data.
- Protecting confidential financial data during computation.
|9||Federated Learning||Trains machine learning models across decentralized edge devices or servers holding local data samples, avoiding centralized data sharing.||- Preserves data locality and privacy, reducing data transfer.|
- Supports collaborative model training on distributed data.
- Suitable for privacy-sensitive applications.
|- Complexity of coordination among edge devices or servers.|
- Potential communication overhead.
- Ensuring model convergence can be challenging.
- Shared models can still leak privacy.
|- Healthcare institutions collaboratively training disease prediction models.|
- Federated learning for mobile applications preserving user data privacy.
- Privacy-preserving AI in smart cities.
|10||Synthetic Data Generation||Creates artificial data that mimics the statistical properties of the original data while protecting privacy.||- Strong privacy protection with high data utility.|
- Preserves data structure and relationships.
- Scalable for generating large datasets.
|- Accuracy and representativeness of synthetic data may vary depending on the generator.|
- May require specialized algorithms and expertise.
|- Sharing synthetic healthcare data for research purposes.|
- Synthetic data for machine learning model training.
- Privacy-preserving data sharing in financial analysis.
|11||Differential Privacy||Provides mathematical privacy metrics by adding carefully calibrated noise to the data.||- Transparency and accountability.|
- Adaptable to various data types and analyses.
- High epsilon values are robust against re-identification attacks.
|- Complex implementation and parameter tuning.|
- Utility loss, especially with high privacy guarantees.
- May not be suitable for all types of data.
|- Protecting individual responses in surveys and questionnaires.|
- Safeguarding user data in data mining and analytics.
- Frequently used to complement other data anonymization tools.
|12||Secure Multiparty Computation (SMPC)||Enables multiple parties to jointly compute functions on their private inputs without revealing those inputs to each other, preserving privacy.||- Strong privacy protection for collaborative computations.|
- Suitable for multi-party data analysis while maintaining privacy.
- Offers security against collusion.
|- Complexity of protocol design and setup.|
- Performance overhead, especially for large-scale computations.
- Requires trust in the security of the computation protocol.
|- Privacy-preserving data aggregation across organizations.|
- Collaborative analytics involving sensitive data from multiple sources.
- Secure voting systems.
The best and the worst data anonymization tools
When it comes to choosing the right data anonymization tools, we are faced with a complex problem requiring a nuanced view and careful consideration. When we put all the Schmäh aside, choosing the right data anonymization tool comes down to balancing the so-called privacy-utility trade-off.
The privacy-utility trade-off refers to the balancing act of data anonymization tools’ two key objectives: providing privacy to data subjects and utility to data consumers. Depending on the specific use case, the quality of implementation, and the level of privacy required, different data anonymization tools are more or less suitable to achieve the ideal balance of privacy and utility. However, some data anonymization tools are inherently better than others when it comes to the privacy-utility trade-off. High utility with robust, unbreakable privacy is the unicorn all privacy officers are hunting for, and since the field is constantly evolving with new types of privacy attacks, data anonymization tools must evolve too.
As it stands today, the best data anonymization tools for preserving a high level of utility while effectively protecting privacy are the following:
Homomorphic encryption allows computations to be performed on encrypted data without the need to decrypt it. This technology is valuable for secure data processing in untrusted environments, such as cloud computing. While it can be computationally intensive, it offers a high level of privacy and maintains data utility for specific tasks, particularly when privacy-preserving machine learning or data analytics is involved. Depending on the specific encryption scheme and parameters chosen, there may be a trade-off between the level of security and the efficiency of computations. Also, increasing security often leads to slower performance.
Utility: can be high, depending on the use case
Federated learning enables machine learning models to be trained across decentralized devices or data sources without centralizing the data. It offers strong privacy guarantees because data remains on the user's device, and only model updates are shared. This approach is well-suited for applications like mobile device usage analytics and personalized recommendation systems.
The level of trade-off can vary depending on factors like the number of participating devices, the quality of local data, and the federated learning algorithms used.
Privacy: high; however, models can leak privacy
Utility: Slightly lower than centralized training
Secure Multiparty Computation (SMPC)
SMPC allows multiple parties to jointly compute a function over their private inputs without revealing those inputs to each other. It offers strong privacy guarantees and can be used for various collaborative data analysis tasks while preserving data utility. SMPC has applications in areas like secure data aggregation and privacy-preserving collaborative analytics.
Utility: can be high, depending on the use case
Synthetic Data Generation
Synthetic data generation techniques create artificial datasets that mimic the statistical properties of the original data. These datasets can be shared without privacy concerns. When properly designed, synthetic data can preserve data utility for a wide range of statistical analyses while providing strong privacy protection. It is particularly useful for sharing data for research and analysis without exposing sensitive information. Synthetic data use cases extend well beyond privacy into the realm of data augmentation.
Utility: high for analytical, data sharing, and ML/AI training use cases
Data anonymization tools: the saga continues
In the ever-evolving landscape of data anonymization tools, the journey to strike a balance between preserving privacy and maintaining data utility is an ongoing challenge. As data grows more extensive and complex and adversaries devise new tactics, the stakes of protecting sensitive information have never been higher.
Legacy data anonymization tools, rooted in manual, rule-based systems, have their limitations and are increasingly likely to fail in protecting privacy. While they may offer simplicity in implementation, they often fall short in preserving the intricate relationships and structures within data.
Modern data anonymization tools, however, present a promising shift towards more robust privacy protection. Privacy-enhancing technologies, including cryptographic and statistical PETs, have emerged as powerful solutions. These tools harness encryption, machine learning, and advanced statistical techniques to safeguard data while enabling meaningful analysis.
Furthermore, the rise of synthetic data generation signifies a transformative approach to data anonymization. By creating artificial data that mirrors the statistical properties of the original while safeguarding privacy, synthetic data generation provides an innovative solution for diverse use cases, from healthcare research to machine learning model training.
As the data privacy landscape continues to evolve, organizations must stay ahead of the curve. What is clear is that the pursuit of privacy-preserving data practices is not only a necessity but also a vital component of responsible data management in our increasingly vulnerable world.