Abstract: Privacy enhancing technologies, such as differentially private stochastic gradient descent (DP-SGD), allow us to access private data without worrying about leaking sensitive information. This is crucial in the modern era of data-centric AI, where all public data has been exhausted and the next frontier models rely on access to high-quality data. A central component in these technologies is private statistical estimation, such as mean estimation and linear regression. We present a series of results where robust statistics and stable…

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