AI RESEARCH
A Comprehensive Guide to Differential Privacy: From Theory to User Expectations
arXiv CS.LG
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ArXi:2509.03294v3 Announce Type: replace-cross The increasing availability of personal data has enabled significant advances in fields such as machine learning, healthcare, and cybersecurity. However, this data abundance also raises serious privacy concerns, especially in light of powerful re-identification attacks and growing legal and ethical demands for responsible data use. Differential privacy (DP) has emerged as a principled, mathematically grounded framework for mitigating these risks.