<ul><li>Privacy-preserving machine learning (PPML) is revolutionizing data-driven applications by ensuring user privacy while harnessing vast datasets.</li><li>Advancements in federated learning (FL), homomorphic encryption (HE), and secure multi-party computation (MPC) technologies are redefining data security in AI applications.</li><li>FL allows decentralized AI model training without sharing raw data, while HE performs computations on encrypted data, and MPC enables joint computations while keeping individual data private.</li><li>PPML is being applied in healthcare, finance, and IoT, offering privacy-preserving diagnostics, personalized recommendations, and secure collaborative data analysis.</li></ul>

Bridging Security and Performance: Innovations in Privacy-Preserving Machine Learning

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