text: 91,143,1494361834932953103,2701532126,Berivan Isik,"Check out our new paper titled “Learning under Storage and Privacy Constraints”. We propose a novel data pre-processing framework, LCoN, which simultaneously boosts data efficiency, privacy, accuracy, and robustness. 1/4 <LINK> #compression #privacy #learning <LINK> Our framework comprises noise injection followed by lossy compression. The noise injection step prevents user information from being leaked during learning, while lossy compression reduces the cost of storing/transmitting the data. 2/4 We show that, when appropriately matching the lossy compression to the distribution of the added noise, the compressed examples converge, in distribution, to that of the noise-free training data. 3/4 With this, we guarantee that the utility of the data for learning is essentially maintained while reducing storage and privacy leakage by quantifiable amounts. The improved robustness against adversarial data is a welcome additional feature we observed empirically. 4/4",https://arxiv.org/abs/2202.02892,"Storage-efficient privacy-guaranteed learning is crucial due to enormous amounts of sensitive user data required for increasingly many learning tasks. We propose a framework for reducing the storage cost while at the same time providing privacy guarantees, without essential loss in the utility of the data for learning. Our method comprises noise injection followed by lossy compression. We show that, when appropriately matching the lossy compression to the distribution of the added noise, the compressed examples converge, in distribution, to that of the noise-free training data. In this sense, the utility of the data for learning is essentially maintained, while reducing storage and privacy leakage by quantifiable amounts. We present experimental results on the CelebA dataset for gender classification and find that our suggested pipeline delivers in practice on the promise of the theory: the individuals in the images are unrecognizable (or less recognizable, depending on the noise level), overall storage of the data is substantially reduced, with no essential loss of the classification accuracy. As an added bonus, our experiments suggest that our method yields a substantial boost to robustness in the face of adversarial test data. ",Learning under Storage and Privacy Constraints,4,"['Check out our new paper titled “Learning under Storage and Privacy Constraints”. We propose a novel data pre-processing framework, LCoN, which simultaneously boosts data efficiency, privacy, accuracy, and robustness. 1/4\n\n<LINK>\n\n#compression #privacy #learning <LINK>', 'Our framework comprises noise injection followed by lossy compression. The noise injection step prevents user information from being leaked during learning, while lossy compression reduces the cost of storing/transmitting the data. 2/4', 'We show that, when appropriately matching the lossy compression to the distribution of the added noise, the compressed examples converge, in distribution, to that of the noise-free training data. 3/4', 'With this, we guarantee that the utility of the data for learning is essentially maintained while reducing storage and privacy leakage by quantifiable amounts. The improved robustness against adversarial data is a welcome additional feature we observed empirically. 4/4']",22,02,972