The Importance of Privacy Measurement in Machine Learning Models

In the age of big data, machine learning (ML) has become a cornerstone of countless industries that are being applied in a wide range of applications. With the ability to process massive datasets and generate insights, ML models have revolutionized how we make decisions, personalize experiences, and even automate entire processes.

For example, companies like Amazon and Netflix use ML algorithms to recommend products and movies based on users' past behaviour. However, the rapid growth and widespread use of these models have also raised significant concerns around privacy and security, mainly related to the inheritance of the training data used during learning.

The Privacy Risks and Regulatory Challenges in Machine Learning

The Cambridge Analytica scandal provides a stark example of the dangers of mishandling personal data. In this case, millions of Facebook users' data was harvested without consent and used to build psychological profiles that targeted voters in the 2016 U.S. presidential election. This misuse of personal data highlighted how machine learning systems, which rely heavily on vast amounts of personal information, can be exploited to influence individuals and manipulate decision-making.

In particular, the vast amounts of personal data required to train these models present a new set of risks. Although “security” is used to refer to both security and privacy as presented here, security and privacy are both profoundly interconnected with protecting ML systems and their data from unauthorized access. This issue is especially critical in sectors such as healthcare and finance, where ML models process highly sensitive personal data. For example, the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. mandates strict privacy protections for individuals' health information. At the same time, the General Data Protection Regulation (GDPR) in the European Union enforces stringent rules on how personal data should be collected, processed, and stored. Both regulations aim to protect individuals' privacy rights, but they also present challenges for organizations looking to use ML for data-driven insights without violating these regulations.

Because of the information they process, ML models are prime targets for malicious actors who may attempt to exploit these systems for illegal purposes. As a result, understanding how to measure and mitigate privacy risks in machine learning is becoming increasingly critical.

This post will first provide some fundamental concepts about attackers to help you understand the security concerns. Then, we will explore a key concept in measuring these risks in machine learning: the privacy risk score. We’ll delve into how this metric helps quantify the risk of privacy leakage, particularly in the context of membership inference attacks, and what both attackers and defenders can do with this information.

The Privacy and Security Challenge in Machine Learning

To understand these machine learning challenges, it's important to note that many ML models rely on vast amounts of data to learn patterns, make predictions, and improve over time. This data often includes personal and sensitive information—medical records, financial transactions, or personal preferences. As a result, security and privacy appear as distinct but interconnected concerns that address different aspects of data protection in ML. While privacy pertains to the governance and responsible handling of personal data used in training and inference processes, security protects the system and its data from unauthorized access, cyberattacks, and misuse.

This context is where the concept of "privacy risk" comes into play. Privacy risk refers to the potential exposure of personal information due to the behaviour of an ML model. If attackers can gain access to or infer which data points were used to train a model, they may be able to steal or misuse sensitive information and potentially expose individuals' private details, leading to severe privacy violations; consequently, identifying these potential risks becomes imperative.

The Threat of Membership Inference Attacks (MIA)

One of the most common ways privacy risks manifest in ML is through membership inference attacks (MIA). In these attacks, the adversary attempts to determine whether a specific data point was part of the model's training dataset (Figure 1). While this may seem minor at first glance, it can have significant consequences when involving sensitive personal data.  The risks are particularly critical when ML models are trained on highly sensitive information, such as medical records, financial transactions, or personal preferences, where even apparently innocent knowledge about an individual's data inclusion can lead to severe privacy breaches.

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The basic idea behind a membership inference attack is that models often exhibit different behaviors for data points they have seen during training compared to those they haven't. This difference is primarily due to:

• Overfitting: If a model has memorized specific data points during training, it may demonstrate higher confidence when making predictions about those data points compared to others it hasn't encountered. This overfitting can cause the model to "recognize" data it has already learned, revealing whether a particular data point was included in the training set.

• Confidence levels: Many models provide probability scores in addition to their predictions. When a model expresses greater confidence about a specific input—indicating it assigns a higher probability to its prediction—it may suggest that the input was part of the training data. In contrast, the confidence level is typically lower for data the model hasn't seen before, revealing an inconsistency that attackers can exploit.

For example, suppose a model has been trained on patient records in a medical dataset. In that case, an attacker may infer whether a particular patient's data was part of the training set by observing the model's responses to queries about that patient's condition. This could lead to the inadvertent exposure of sensitive information, like whether a person's medical history was part of the data used to train the model. Consequently, knowing whether a patient's data was included in the training set could reveal private health details that the model was exposed to during training, violating privacy.

Similarly, if a model learns from transaction data in financial services, an adversary could exploit this information to discern a person's economic behaviour, potentially leading to identity theft or fraud.

Such breaches highlight the critical need to assess and manage privacy risks throughout the machine learning pipeline, from data collection and preprocessing to model deployment and querying.

Measuring security: the privacy risk score

Given the significant risks posed by membership inference attacks, detecting and mitigating potential vulnerabilities effectively is imperative, especially when sensitive data is involved. One of the most practical ways to do this is by using the privacy risk score, a powerful metric tool for both attackers and defenders introduced by Song and Mittal in this publication.

Unlike traditional MIA metrics, which focus on quantifying the efficacy of the attackers and defenders, the privacy risk score measures the probability that a given sample was part of the training set.

Formally, the privacy risk score quantifies the likelihood that a given data point is part of a machine learning model's training set by analyzing how it behaves when it processes that data. Specifically, this score is defined as the posterior probability that a sample comes from the training set based on the model's behaviour when queried with that sample. The calculation utilizes Bayes' theorem, which combines prior probabilities (the likelihood of the sample being from the training or test set) with conditional distributions (the likelihood of the model's response given the sample's origin).

A shadow model technique is also employed to compute the privacy risk score. This technique aims to mimic the behaviour of the target model and is used to estimate the conditional probabilities required for the empirical calculation of the metric. The accuracy of these estimates largely depends on how closely the shadow model resembles the target model and the quantity of available shadow data. For a more comprehensive understanding of the mathematical foundations and detailed workings of the privacy risk score, we recommend consulting the original publication, which explains these aspects more thoroughly.

An interesting feature of this score is that it can help attackers and defenders better understand and mitigate the risks associated with membership inference attacks. For example:

For Attackers:

Instead of blindly trying to perform attacks on all data points, attackers used for testing models can use the privacy risk score to focus on the samples with the highest risk of being members of the training dataset.

Thus, attackers can use this score to target their efforts on the most vulnerable data points, increasing the accuracy and impact of their attacks and identifying the models’ vulnerabilities.

For Defenders:

Conversely, the privacy risk score can also be used to assess which samples are most at risk of exposure. Defenders can implement targeted strategies to safeguard these high-risk samples by identifying them.

Moreover, defenders can use privacy risk scores to identify overall vulnerabilities in the model. By analyzing the output probabilities and privacy risk distribution, they can experiment with different configurations, such as adjusting the model's complexity or retraining it with more diverse data, to minimize the risk of inadvertent data leakage.

Thus, as we can see, the privacy risk score helps practitioners develop and audit more robust and trustworthy ML models.

In this context, if you look for a practical use of this metric, you can find a practical implementation of the privacy risk score within the holisticai Python package to demonstrate how the privacy risk score can reveal crucial security insights in classification tasks and can help you to better understand how privacy risk score metric can be used to obtain interesting security insights from ML models.

Final Thoughts

As machine learning continues to permeate every aspect of our lives, privacy will remain among the most critical concerns in its development and deployment. Membership inference attacks highlight the vulnerabilities inherent in ML systems, and the privacy risk score provides a valuable way to measure and mitigate these risks.

To fully harness the power of AI and machine learning while maintaining user trust, it is essential to prioritize privacy in every stage of model development—from data collection and training to deployment and post-deployment monitoring. Besides incorporating measurement tools like the privacy risk score, we can obtain a complete pipeline of security in machine learning and have a deeper analysis of our systems for defenders or attackers.

If you want to extend your knowledge about the foundations and proofs that support this metric, we recommend always reviewing the original publication.