Protecting against membership inference attacks
Membership inference attacks pose a significant threat to the privacy of individuals in machine learning systems. These attacks aim to determine whether a specific data point was part of the training dataset used to create a machine learning model, potentially exposing sensitive information about individuals. To mitigate the risk of such attacks, differential privacy techniques can be employed.
To protect against membership inference attacks using differential privacy, several approaches can be adopted:
- Noise addition: During the training process, noise is added to the computations to introduce randomness and mask individual data points. This makes it challenging for attackers to identify whether a specific data point was used in the training.
- Privacy budget management: Differential privacy operates under a privacy budget that determines the maximum amount of privacy loss allowed. By carefully managing and allocating the...
