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Synthetic Data vs Data Masking: Benefits & Challenges in 2024

Protecting sensitive data with the correct solution from insider threats is critical in many areas, including healthcare, finance, and government. Further than their immeasurable destruction to the confidentiality of customers and thus to the reputation of the businesses, data breaches in these sectors can also be extremely costly. IBM reports that the global average total cost of a data breach is 4,35 million dollars.1 Moreover, by 2025, it is expected that 30% of critical infrastructure organizations will experience an operation-halting security breach.2  

Synthetic data and data masking are two methods that are commonly used to protect sensitive information in databases. In this article, we will:

  • briefly describe them and explain how they work
  • compare the benefits and challenges/limitations of each method
  • compare which one is better in overall
  • investigate when and where to use each one 

Synthetic Data vs Data Masking: How do they work?

(Source: Practical Synthetic Data Generation, page 2)3

Synthetic data is a type of artificial data that is generated using algorithms and statistical models. It is a method primarily implemented for data privacy. It is designed to closely mimic real data in terms of statistical properties and relationships, but without containing any sensitive information.

Figure 2

Data masking, on the other hand, is a technique that is used to protect sensitive data by obscuring or replacing its original values with randomized or fictitious data. Data masking is typically performed on a copy of the original data set, leaving the original data intact and unmodified. The goal of data masking is to prevent unauthorized access to sensitive data by making it unreadable or unrecognizable to unauthorized people.

There are several types of data masking methods that can be used, depending on the sensitivity of the data and the level of protection that is required. For example, data masking can function by replacing sensitive data with fictitious data that follows a similar distribution, such as using random numbers or fake names instead of real ones. It can also work by replacing sensitive data with generic or irrelevant data, such as using random numbers or dummy text instead of real data. Data masking can also encrypt sensitive data, making it unreadable.

Benefits and challenges of Synthetic Data and Data Masking

Both synthetic data and data masking have their own features, benefits, and challenges. Here is a list of the benefits and challenges of each. 

Synthetic Data

Benefits

  1. Synthetic data can be shared and used for research, testing, or training purposes without violating any privacy laws or ethical standards; for example, for training deep learning models. This is because synthetic data is generated by a computer algorithm and does not contain any real or sensitive information, making it safe to use and distribute without any legal or ethical concerns.
  2. Synthetic data can be used to improve the quality and generalizability of data-driven models. This is because synthetic data is generated using statistical methods and machine learning techniques, which can create data that is more diverse and balanced than the original data. This can help data-driven models to perform better and be more reliable when applied to new or unobserved data.
  3. Synthetic data can be generated on demand and at scale. This is because synthetic data is generated by a computer algorithm, which can be run multiple times and with different parameters to create different versions of the same data set. This allows organizations to create large and diverse data sets that can be used for different purposes, such as testing, training, or validation.
  4. Synthetic data can be customized and tailored to specific needs. This is because synthetic data is generated by a computer algorithm, which can be fine-tuned and adjusted to create data that is similar to the original data but with different characteristics or properties. For example, synthetic data can be generated to have different distributions, correlations, or patterns than the original data, which can be useful for testing the performance or robustness of data-driven models under different scenarios.

Challenges

  1. The quality of synthetic data depends on the accuracy and robustness of the underlying algorithms and dataset. This means that synthetic data may not always be representative of the original data, especially if the data set is highly skewed or contains rare events or outliers. This can affect the performance or interpretability of data-driven models that are trained on synthetic data.
  2. Synthetic data may not always preserve the unique characteristics or relationships of the original data. This means that synthetic data may not capture the nuances and complexities of the original data.
  3. Synthetic data may not always be realistic or believable. This means that synthetic data may not always look or behave like real data, which can make it difficult to use for testing, training, or validation purposes.
  4. Synthetic data may not always be compatible or interoperable with other data sets or systems. This means that synthetic data may not always be able to integrate or interact with other data sets or systems, which can limit its usefulness or applicability in real-world scenarios.

Data Masking

Benefits

  1. Data masking can protect sensitive data from unauthorized access or disclosure. This is because data masking involves obscuring or replacing sensitive data with randomized or fictitious data, which makes it unreadable or unrecognizable to anyone who is not authorized to see it. This can help organizations to comply with data privacy laws and regulations of GDPR and CCPA, and to prevent the misuse or abuse of sensitive data.
  2. Data masking can preserve the integrity and consistency of the original data. This is because data masking is typically performed on a copy of the original data set, leaving the original data intact and unmodified. This can help organizations to maintain the accuracy and reliability of the original data, and to avoid any errors or inconsistencies that may arise from modifying the data directly.
  3. Data masking can be customized and tailored to specific needs. This is because data masking involves applying different masking methods to different data fields, depending on their sensitivity and the level of protection that is required. For example, data masking can be used to mask sensitive data such as names, addresses, or social security numbers, but to leave other data fields such as dates, numbers, or codes unchanged. This can help organizations to balance the need for data privacy and security with the need for data usability and accessibility.
  4. Data masking can be reversible. This is because data masking involves applying a reversible transformation to the data, which can be undone if necessary. This can be useful in scenarios where the masked data needs to be accessed or used for a specific purpose, such as testing, training, or validation. This can help organizations to retain the flexibility and adaptability of the data, and to avoid any data loss or corruption that may arise from irreversible masking methods.

Challenges

  1. Data masking may not always provide complete protection for sensitive data. This is because data masking involves obscuring or replacing sensitive data with randomized or fictitious data, which may not always be sufficient to prevent the sensitive data from being inferred or reconstructed by an attacker. This can be a particular concern for data masking methods that use simple or predictable masking patterns, or that do not take into account the correlations or dependencies between different data fields.
  2. Data masking may not always preserve the usefulness or value of the original data. Due to the obscuring or replacing sensitive data with randomized or fictitious data, it may not always retain the same statistical properties or patterns as the original data.
  3. Data masking may not always be reversible. This is because data masking involves applying a transformation to the data, which may not always be reversible if the original data is lost or corrupted. This can be a particular concern for data masking methods that use irreversible transformations, such as encryption or hashing, which may not allow the original data to be recovered or accessed.
  4. Data masking may not always be compatible or interoperable with other data sets or systems. This is because data masking involves applying a specific transformation to the data, which may not always align with other data sets or systems that use different formats, structures, or standards. This can affect the ability of organizations to integrate or exchange data across different systems, or to use the masked data for different purposes or applications.
Figure 3

When to use Synthetic Data and when to use Data Masking?

Synthetic data and data masking can be used in different contexts and for different purposes. In general, synthetic data is often used when the goal is to test or evaluate data-driven models and algorithms, while data masking is often used when the goal is to protect sensitive data during non-production activities.

Synthetic Data

  • Test data-driven models and algorithms: One common use case for synthetic data is in testing and evaluating data-driven models and algorithms. Because synthetic data is generated using algorithms and statistical models, it can be used to test and evaluate data-driven models and algorithms in a way that is similar to using real data, but without exposing sensitive information. This makes synthetic data a useful tool for ensuring the accuracy and reliability of data-driven models and algorithms without exposing sensitive information.
  • Train machine learning models: Another common use case for synthetic data is in training machine learning models. Because synthetic data can be generated in large quantities and can be tailored to specific training tasks and objectives, it can be used to train machine learning models without requiring access to sensitive data. This is particularly useful in situations where access to sensitive data is limited or restricted, such as in healthcare or finance.

Data Masking

  • Protect sensitive data: Data masking, on the other hand, is often used when the goal is to protect sensitive data during non-production activities. This might include development, testing, or other activities where sensitive data is used, but where the data itself is not the primary focus. In these cases, data masking can be used to alter sensitive data in a way that makes it difficult to use or recognize, while still retaining its original format and meaning. This allows the data to be used for its intended purpose, while protecting it from unauthorized access or misuse.
  • Comply with regulations: GDPR and CCPA regulations generally enforce and motivate the use of data masking for customer rights. In this sense, we can say that data masking is a more secure way to use real data while complying with regulations.

Which one is better: Synthetic Data or Data Masking?

As mentioned earlier, synthetic data and data masking are two different approaches to protecting sensitive data, and each has its own advantages and disadvantages. In general, synthetic data is often preferred when the goal is to test or evaluate data-driven models and algorithms, while data masking is often preferred when the goal is to protect sensitive data during non-production activities.

Advantages of Synthetic Data over Data Masking

  • One of the main advantages of synthetic data is that it can be used to test and evaluate data-driven models and algorithms without exposing sensitive information.
  • Another advantage of synthetic data is that it can be used to train machine learning models.

Advantages of Data Masking over Synthetic Data

  • Data masking has the advantage of retaining the original structure and meaning of the data compared to synthetic data. 

Overall, it can be concluded that for better privacy and ethical purposes, synthetic data may be a better option as it only mimics the real dataset, compared to the altering of only sensitive data in data masking. On the other hand, if the purpose is to hold the original data as much as possible without revealing certain important information, then data masking is the right option. 

For more on Synthetic Data and Data Masking

If you want to learn more about these technologies, you can check our other detailed and related articles:

If you have questions regarding synthetic data and data masking, feel free to contact us:

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Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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