Businesses are using machine learning (ML) to unlock valuable insights, gain operational efficiencies, and solidify competitive advantage. Recent developments in generative artificial intelligence and ML have illuminated the need for privacy and security. ML models are algorithms that process data to generate insights and inform decisions. ML is able to learn and improve through data. When these data sources contain sensitive or proprietary information, using them for ML model training raises privacy and security concerns. Any vulnerability of the model itself becomes a liability for the entity using it, which increases the organization’s risk profile. This issue is one of the main barriers preventing broader use of ML today.
Vulnerabilities in ML models typically lead to two macro categories of attack vectors: model inversion and model spoofing. Model inversion attacks involve targeting the model itself to reverse engineer back to the data over which it was trained. Model spoofing 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. Privacy enhancing technologies (PETs) are used to address these vulnerabilities head on. These powerful technologies allow businesses to encrypt sensitive ML models, run and/or train them, and extract valuable insights while eliminating the risk of exposure. Advancements in PETs are providing a promising path forward.
Read more: https://www.helpnetsecurity.com/2023/08/28/machine-learning-ml-models/