Data privacy is a big deal in machine learning, especially when it comes to large language models (LLMs) that are trained using huge amounts of data, often containing personal information.

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One common approach to protect privacy is data masking, which involves hiding or removing sensitive details from the data. But when it comes to language models, data masking doesn’t quite cut it. Here’s why.

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‍The Problem with Data Masking

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At first glance, data masking seems like a reasonable way to handle privacy. The idea is to find sensitive information in the data and hide or remove it before training the model. This works pretty well for things like credit card numbers or social security numbers—things that follow predictable patterns. But language is much messier and more complex.

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Natural language, unlike structured data, doesn’t fit neatly into boxes. People don’t always reveal personal information in obvious ways. Sure, you can remove someone’s name or address from text, but personal details often show up in less direct ways.

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For instance, consider someone described as the "top scorer of the national legal bar exam in 2022." Even if their name is masked, that specific detail could still be traced back to a public record or article, making it possible to identify the individual. This example shows how context can undermine the effectiveness of data masking.

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Context Matters

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One of the biggest challenges with data masking in language models is that it ignores the importance of context. People share different kinds of information depending on the situation. You might be okay talking about your health in a support group but would feel uncomfortable if your employer found out about it. Language models don’t have the ability to understand these nuances, so they can easily mishandle private information.

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Take the example of someone named Emma who’s talking about being a whistleblower in a private conversation. Even if a data masking tool removes explicit mentions of her whistleblowing, other clues—like workplace issues, meetings with lawyers, or sudden career changes—could still be there, making it easy for someone to figure out what she’s talking about. Data masking isn’t enough to protect Emma’s privacy because it doesn’t grasp the full picture.

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Hard-to-Identify Information

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Another issue with data masking is that a lot of personal information doesn’t follow a specific format, making it harder to detect. In structured data, patterns are easier to spot—like a series of digits for a phone number or social security number. But with free-form text, people can describe personal things in countless ways, including metaphors or vague language.

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For example, someone on an online forum might talk about a rare medical condition without ever naming it. They might describe the symptoms, treatments, or how it affects their daily life. A data masking tool could easily miss this, exposing sensitive health information even though it wasn’t explicitly mentioned.

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The Limits of Data Masking for Language Models

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Language models are used in all sorts of applications—from search engines to customer service chatbots. If they’re trained on data that wasn’t properly masked, they can accidentally leak private information.

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For example, a healthcare chatbot trained on unmasked data might reveal details about a person’s medical treatments, pregnancies, or family history, even if names and other obvious identifiers were removed. This could lead to serious privacy breaches and other problems like discrimination or harm to someone’s career or relationships.

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In the end, while data masking might offer some protection in specific situations, it’s not enough to safeguard privacy in language models. These systems need a much deeper understanding of context and human communication, something current methods like data masking can’t provide. To really protect user privacy, we need new approaches, starting with using data that’s meant for public use and developing techniques that account for the complexity of how people talk and share information.

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Data Masking Isn’t Enough—And Regulators Know It

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Regulators are increasingly focused on preventing sensitive data leaks, especially when it comes to contextual information that data masking fails to protect. While masking may hide obvious identifiers like names or numbers, it doesn’t account for subtle details in natural language that can still expose private information. This puts companies at significant risk of both regulatory penalties and severe reputational damage. A single privacy breach can lead to hefty fines and destroy customer trust, with long-lasting consequences.

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Ready to Experience True Privacy Protection?

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At Secludy, we do things differently. We help companies generate synthetic data that mimics the original dataset using an advanced method known as differential privacy, which offers privacy guarantees. Unlike data masking, our approach not only protects individual data points but also ensures that the overall meaning and context remain private—no matter how complex the information.

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Interested in learning more?

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Book a demo today and see how Secludy can take your data privacy to the next level by providing real-world, robust protection for your AI and machine learning projects.