"Uncovering a Privacy-Preserving Approach to Machine Learning"

In the era of data-driven decision-making, businesses are leveraging the power of Machine Learning (ML) to uncover valuable insights, increase operational efficiencies, and solidify their competitive advantage. Even though recent advancements in generative Artificial Intelligence (AI) have raised awareness about the power of AI/ML, they have also shed light on the need for privacy and security. Groups such as IAPP, Brookings, and Gartner's recent AI TRiSM framework have outlined key considerations for organizations seeking to achieve the business outcomes made possible by AI without increasing their risk profile. ML model security is at the forefront of these requirements. Privacy-preserving ML has emerged as a means to ensure that users can maximize the potential of ML applications in this increasingly crucial field. This article continues to discuss using ML to generate insights, vulnerabilities in ML models, and privacy-enhancing technologies. 

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