Beyond the Algorithm: AI, Security, Privacy, and Ethics by Unknown

Author:Unknown
Language: eng
Format: epub
Publisher: Addison-Wesley Professional
Published: 2024-01-23T00:00:00+00:00

Real-World Examples of Membership Inference Attacks

In one study, researchers demonstrated that membership inference attacks could be used to identify whether a patient’s data was included in a training set for a genetic disease prediction model.

Membership inference attacks could also be used to infer whether an individual’s location data was used in the training of a geolocation prediction model, revealing sensitive information about that person’s movements.

Membership inference attacks pose a significant risk to privacy, particularly when AI and ML models are trained on sensitive data. They can lead to the exposure of personal information, a violation of privacy laws, and even pose potential harm to individuals if their data are discovered to be part of certain datasets (e.g., a dataset used to train a mental health prediction model).

Defending against membership inference attacks involves balancing model performance with privacy preservation. Techniques include differential privacy, model generalization, and robust learning algorithms. Ensuring a diverse and extensive training dataset can also help in mitigating these attacks.

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.