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Label-Only Model Inversion Attacks via Knowledge Transfer Supplementary Materials
It's important to note that
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- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.68)
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Limits of Private Learning with Access to Public Data
Raef Bassily, Shay Moran, Noga Alon
Neural Information Processing Systems http://nips.cc/
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Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited
Di Wang, Marco Gaboardi, Jinhui Xu
In this paper, we revisit the Empirical Risk Minimization problem in the noninteractive local model of differential privacy. In the case of constant or low dimensions (pn), we first show that if the loss function is(,T)-smooth, wecanavoidadependence ofthesample complexity,toachieveerrorα,onthe exponential of the dimensionalityp with base1/α (i.e.,α p), which answers a questionin[19].
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Public-data Assisted Private Stochastic Optimization: Power and Limitations
We study the limits and capability of public-data assisted differentially private (P A-DP) algorithms. Specifically, we focus on the problem of stochastic convex optimization (SCO) with either labeled or unlabeled public data.
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Public-data Assisted Private Stochastic Optimization: Power and Limitations
We study the limits and capability of public-data assisted differentially private (PA-DP) algorithms. Specifically, we focus on the problem of stochastic convex optimization (SCO) with either labeled or unlabeled public data.