MIT develops efficient approach to safeguard sensitive AI training data

MIT researchers have developed a new framework designed to balance the need for data protection with the performance of artificial intelligence (AI) models. The article highlights technical deficiencies in existing security techniques that, while protecting sensitive user data from extraction by attackers, often reduce model accuracy. Instead, the researchers built their Privacy PAC framework around a novel metric that aims to ensure sensitive information, such as medical images or financial records, remains secure without significantly compromising the AI model's effectiveness. The MIT team claim their approach can be applied to privatise virtually any algorithm, with no requirement to access the internal workings of the algorithm, making it a versatile tool for enhancing data security across various AI applications.