ML04:2023 Membership Inference Attack
Description
Membership inference attacks occur when an attacker manipulates the model’s training data in order to cause it to behave in a way that exposes sensitive information.
How to Prevent
Model training on randomized or shuffled data: Training machine learning models on randomized or shuffled data can make it more difficult for an attacker to determine whether a particular example was included in the training dataset.
Model Obfuscation: Obfuscating the model’s predictions by adding random noise or using differential privacy techniques can help prevent membership inference attacks by making it harder for an attacker to determine the model’s training data.
Regularisation: Regularisation techniques such as L1 or L2 regularization can help prevent overfitting of the model to the training data, which can reduce the model’s ability to accurately determine whether a particular example was included in the training dataset.
Reducing the training data: Reducing the size of the training dataset or removing redundant or highly correlated features can help reduce the information an attacker can gain from a membership inference attack.
Testing and monitoring: Regularly testing and monitoring the model’s behavior for anomalies can help detect and prevent membership inference attacks by detecting when an attacker is attempting to gain access to sensitive information.
Risk Factors
| Threat Agents/Attack Vectors | Security Weakness | Impact |
|---|---|---|
| Exploitability: 4 (Moderate) ML Application Specific: 5 ML Operations Specific: 3 |
Detectability: 3 (Moderate) | Technical: 4 (Moderate) |
| Threat Actors: Hackers or malicious actors who have access to the data and the model. Insiders who have malicious intent or are bribed to interfere with the data. Attack Vectors: Unsecured data transmission channels that allow unauthorized access to the data. |
Lack of proper data access controls. Lack of proper data validation and sanitization techniques. Lack of proper data encryption. Lack of proper data backup and recovery techniques. |
Unreliable or incorrect model predictions. Loss of confidentiality and privacy of sensitive data. Legal and regulatory compliance violations. Reputational damage. |
It is important to note that this chart is only a sample based on the scenario below only. The actual risk assessment will depend on the specific circumstances of each machine learning system.
Example Attack Scenarios
Scenario #1: Inferencing financial data from a machine learning model
A malicious attacker wants to gain access to sensitive financial information of individuals. They do this by training a machine learning model on a dataset of financial records and using it to query whether or not a particular individual’s record was included in the training data. The attacker can then use this information to infer the financial history and sensitive information of individuals.
The attacker executed this attack by training a machine learning model on a dataset of financial records obtained from a financial organization. They then used this model to query whether or not a particular individual's record was included in the training data, allowing them to infer sensitive financial information.
References: