Security News > 2023 > August > Uncovering a privacy-preserving approach to machine learning

Machine learning models are algorithms that process data to generate meaningful insights and inform critical business decisions.

When these data sources contain sensitive or proprietary information, using them for machine learning model training or evaluation/inference raises significant privacy and security concerns.

Vulnerabilities in ML models typically lead to two macro categories of attack vectors: model inversion and model spoofing.

Model spoofing, on the other hand, represents a form of adversarial machine learning wherein an attacker attempts to deceive the model by manipulating the input data in such a manner that the model makes incorrect decisions aligned with the attacker's intentions.

This approach to training models ensures privacy, security, and confidentiality while harnessing the collective power of diverse datasets to enhance the accuracy and effectiveness of machine learning models.

The increasing reliance on machine learning to enhance business activity is not a passing trend - and neither are the significant risks associated with ML models.

News URL

https://www.helpnetsecurity.com/2023/08/28/machine-learning-ml-models/