The impact of data breaches in machine learning model development

What is a data breach?

Technical errors are common during the development of machine learning models. Even unintentional errors can be discovered through inspection. Because most errors are reflected directly in the model's performance, their impact is easily noticeable. However, the effects of a data breach are more insidious. Unless a model is deployed to the public, its existence is difficult to detect. Because the situations faced by the model in real-life scenarios are invisible.

The data breach may give the modeler the illusion that the model has achieved the optimal state it has been looking for through extremely high evaluation metrics in both data sets. However, once the model is put into production, not only is its performance likely to be worse than it was during the test run, but it also requires more time to check and tune the algorithm. As a machine learning modeler, you may face contradictory results during the development and production phases.

Causes and Effects of Data Leakage

The introduction of this information is unintentional and occurs during the data collection, aggregation and preparation process. It is often subtle and indirect, making it difficult to detect and eliminate. During training, the model captures correlations or strong relationships between this additional information and target values ​​to learn how to make predictions. However, once released, this additional information is not available, leading to model failure.

During the data aggregation and preparation stages, some statistical transformations, such as interpolation and data scaling, are sometimes applied that exploit statistical data distributions. Therefore, we cannot obtain the same results if we apply these corrections to the entire dataset before processing the training and test sets. In this case, the distribution of the test data will affect the distribution of the training data.

For example, we can think of time series data as a data sequence containing 100 values ​​of a feature. If we divide this sequence into 2 identical groups of 50 values, then the statistical properties such as mean and standard deviation of the two groups will not be the same. In time series forecasting tasks, we can apply k-fold cross-validation to evaluate the performance of the model. This process may introduce past data instances in the validation set and future instances in the training set.

Similarly, in actual production environments, machine learning models without data leaks often perform better than test results and are less affected by data leaks.

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