Although recent developments in generative artificial intelligence (AI) have raised unprecedented awareness around the power of AI/ML, they have also illuminated the foundational need for privacy and security. Groups like IAPP, Brookings, and Gartner’s recent AI TRiSM framework have outlined key considerations for organizations looking to achieve the business outcomes uniquely available through AI without increasing their risk profile. At the forefront of these imperatives is ML model security. Directly addressing this key area, privacy-preserving machine learning has emerged as a path to ensure that users can capitalize on the full potential of ML applications in this increasingly important field. Machine learning models are algorithms that process data to generate meaningful insights and inform critical business decisions. What makes ML remarkable is its ability to continuously learn and improve. When a model is trained on new and disparate datasets, it becomes smarter over time, resulting in increasingly accurate and valuable insights that were previously inaccessible. These models can then be used to generate insights from data, which is referred to as model evaluation or inference. To deliver the best outcomes, models need to learn and/or be leveraged over a variety of rich data sources. 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.
Full opinion : Uncovering a privacy-preserving approach to machine learning.
