Data Leakage

Extracted Attributes

• Alternative Names

  Target leakage, train-test contamination, data leak

• Data Security Definition

  The unintentional or accidental release or exposure of sensitive, confidential, or protected data to unauthorized individuals.

• Common Causes in Security

  Human error, misconfigured cloud storage, insecure databases, lost devices, and sharing sensitive information over insecure channels.

• Types in Machine Learning

  Target leakage and train-test contamination.

• Machine Learning Definition

  The use of information during model training that would not be available at prediction time, leading to overfitting and inaccurate production results.

Timeline

• Volkswagen Group of America discloses a data leak exposing information of approximately 3.3 million individuals due to an unsecured database left exposed by a third-party vendor. (Source: BlueVoyant)

  2021-06-01

• A review finds data leakage to be a widespread failure mode in machine-learning-based science, affecting at least 294 academic publications across 17 disciplines. (Source: Wikipedia)

  2023-01-01

• A Microsoft leak exposes 38 TB of sensitive internal data, including private keys, passwords, and open-source AI training data, due to a misconfigured Azure Blob Store. (Source: IBM)

  2023-09-01

Leakage (machine learning)

In statistics and machine learning, leakage (also known as data leakage or target leakage) refers to the use of information during model training that would not be available at prediction time. This results in overly optimistic performance estimates, as the model appears to perform better during evaluation than it actually would in a production environment. Leakage is often subtle and indirect, making it difficult to detect and eliminate. It can lead a statistician or modeler to select a suboptimal model, which may be outperformed by a leakage-free alternative.

Web Search Results

• Leakage (machine learning) - Wikipedia

  In statistics and machine learning, leakage (also known as data leakage or target leakage) refers to the use of information during model training that would not be available at prediction time. This results in overly optimistic performance estimates, as the model appears to perform better during evaluation than it actually would in a production environment. Leakage is often subtle and indirect, making it difficult to detect and eliminate. It can lead a statistician or modeler to select a suboptimal model, which may be outperformed by a leakage-free alternative. ## Leakage modes Leakage can occur at multiple stages of the machine learning workflow. Broadly, its sources can be divided into two categories: those arising from features and those arising from training examples. [...] A 2023 review found data leakage to be "a widespread failure mode in machine-learning (ML)-based science", having affected at least 294 academic publications across 17 disciplines, and causing a potential reproducibility crisis. ## Detection Data leakage in machine learning can be detected through various methods, focusing on performance analysis, feature examination, data auditing, and model behavior analysis. Performance-wise, unusually high accuracy or significant discrepancies between training and test results often indicate leakage. Inconsistent cross-validation outcomes may also signal issues.

• What Is Data Leakage?

  On the other hand, Data Leakage refers to the unintentional or accidental release or exposure of sensitive data from within an organization. This can occur due to various reasons, such as human error, system vulnerabilities, or lack of secure procedures in handling sensitive data. While data leakage may sometimes be a result of malicious activities, it is often not a deliberate act. The primary difference between these two events lies in intent and method. Data exfiltration is a deliberate act of extracting data without authorization, often involving sophisticated techniques to remain undetected. In contrast, data leakage is usually unintentional and happens due to insufficient security measures, negligence, or accidents. ## How to Prevent Data Leakage? [...] This blog post discusses data leakage and explores its different types - physical exposure, malicious insider activity, and accidental leakage. We share some notable examples demonstrating how simple mistakes or oversights can lead to severe consequences. We also compare data leakage with concepts like data breach and exfiltration to help you better understand these concepts. ## What Is Data Leakage? Data leakage is the unintentional exposure of sensitive, confidential, or protected information to unauthorized individuals due to internal errors or misconfigurations. It often occurs when data leaves its intended environment through insecure channels such as misconfigured cloud storage, emails, or removable media. ## Types of Data Leakage [...] ## What Is a Data Leakage Prevention Policy? A Data Leakage Prevention (DLP) Policy is a framework that guides an organization's efforts to prevent the unauthorized access, use, disclosure, disruption, modification, or destruction of sensitive information. This policy includes identifying sensitive data, monitoring its use and movement, and preventing unauthorized access or sharing. It keeps sensitive data, such as personally identifiable information (PII), protected health information (PHI), and intellectual property, safe from cyber threats. Key points to consider in a DLP policy:

• What is Data Leakage? | IBM

  A frequently repeated real-world instance of data leakage is the inadvertent exposure of sensitive PII in unencrypted data storage environments. This data can include phone numbers, social security numbers and credit card details, which hackers can use for identity theft or fraudulent transactions. Leaked data can also be exploited in ransomware attacks, where bad actors encrypt the exposed information and demand payment for its release, often after gaining access through a faulty system or a successful phishing scam. A Microsoft leak in 2023 exposed 38 TB of sensitive internal data due to a misconfigured Azure Blob Store, a type of object storage. This data included confidential information such as personal data, private keys, passwords and open source AI training data. [...] ## Data leakage in machine learning In the context of machine learning, the term “data leakage” has a distinct meaning compared to its general use in data security and loss prevention. Data leakage refers to improperly introducing information from outside the training dataset into the model during its development, which can lead to overoptimistic and misleading results. This type of data leakage occurs when machine learning algorithms are trained on data they should not have access to, resulting in a model that performs exceptionally well in development but fails in real-world applications. [...] # What is data leakage? Aerial view of a river and its branching tributaries ## What is data leakage? Data leakage occurs when sensitive information is unintentionally exposed to unauthorized parties. For example, a misconfigured cloud storage server might allow easy access to personally identifiable information (PII) and trade secrets. The most common vectors for data leakage stem from human error such as an employee misplacing their laptop or sharing sensitive information over email and messaging platforms. Hackers can use exposed data to commit identity theft, steal credit card details or sell the data on the dark web. ## Would your team catch the next zero-day in time?

• What is Data Leakage in Machine Learning?

  ## Impact of data leakage on machine learning models Data leakage is a common pitfall in training machine learning algorithms for predictive modeling. A National Library of Medicine study1 found that across 17 different scientific fields where machine learning methods have been applied, at least 294 scientific papers were affected by data leakage, leading to overly optimistic performance. A Yale study2 found that data leakage can either inflate or deflate performance metrics of neuroimaging-based models, depending on whether the leaked information introduces noise or creates unrealistic patterns. These models are used for diagnosing illness to identify treatments and help neuroscientists better understand the relationship between brain and body. [...] ## Thank you! You are subscribed. ## Examples and types of data leakage Data leakage happens when data from outside the training dataset is used to create the model, but this future data will not be available when the model is used for prediction. The model will perform well in testing and validation, but when used in production, it becomes entirely inaccurate. There are two types of leakage: target leakage and train-test contamination. Target leakage: Models include data that will not be available when the model is used to make predictions. Using information that won't be available during real-world predictions leads to overfitting, where the model performs exceptionally well on training and validation data but poorly in production. [...] # What is data leakage in machine learning? Tim Mucci IBM Writer ## What is data leakage in machine learning? Data leakage in machine learning occurs when a model uses information during training that wouldn't be available at the time of prediction. Leakage causes a predictive model to look accurate until deployed in its use case; then, it will yield inaccurate results, leading to poor decision-making and false insights.

• Data Leakage: Common Causes, Examples & Tips for Prevention

  Skip to main content BlueVoyant Blog Contact us Customer login Request a demo BlueVoyant Menu ## BlueVoyant 1. Data Leakage # Data Leakage: Common Causes, Examples & Tips for Prevention ## What Is Data Leakage? Data leakage occurs when sensitive data gets unintentionally exposed to the public in transit, at rest, or in use. Here are common examples: Data exposed in transit — Data transmitted via emails, API calls, chat rooms, and other communications. Data exposed at rest — Can occur due to misconfigured cloud storage, insecure databases, or unattended or lost devices. Data exposed in use — Such as data on printers, screenshots, clipboards, and USB drives. [...] Data breaches occur due to unknown hardware, software, or security infrastructure vulnerabilities or unforeseen risks. It requires cybercriminals to find a vulnerability and exploit it. Administrators mitigate this risk by continually updating outdated software and immediately installing security patches or updates. A data leak can result in a data breach but does not require exploiting unknown vulnerabilities. A human error is usually the culprit behind a data leak. For example, a misconfigured Amazon Web Services (AWS) S3 bucket can cause a leak. S3 buckets provide cloud storage space for uploading files and data. [...] ## 5 Data Leakage Examples ### 1. Volkswagen Group of America Volkswagen disclosed a data leak in June 2021 — malicious actors exploited an unsecured third-party vendor to obtain data about Canadian and US customers. Between 2014 and 2019, the company collected data mainly for marketing and sales purposes. However, Volkswagen failed to protect this database, leaving it exposed from August 2019 to May 2021 and allowing the leak of information about around 3.3 million individuals. Driver’s licenses and car numbers were exposed during the leak, as well as the loan and social insurance numbers of a smaller set of customers. ### 2. Infinity Insurance Company