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• W2533156578 abstract "In settings where data instances are generated sequentially or in streaming fashion, online learning algorithms can learn predictors using incremental training algorithms such as stochastic gradient descent. In some security applications such as training anomaly detectors, the data streams may consist of private information or transactions and the output of the learning algorithms may reveal information about the training data. Differential privacy is a framework for quantifying the privacy risk in such settings. This paper proposes two differentially private strategies to mitigate privacy risk when training a classifier for anomaly detection in an online setting. The first is to use a randomized active learning heuristic to screen out uninformative data points in the stream. The second is to use mini-batching to improve classifier performance. Experimental results show how these two strategies can trade off privacy, label complexity, and generalization performance." @default.
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• W2533156578 date "2016-10-28" @default.
• W2533156578 modified "2023-09-25" @default.
• W2533156578 title "Differentially Private Online Active Learning with Applications to Anomaly Detection" @default.
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• W2533156578 doi "https://doi.org/10.1145/2996758.2996766" @default.
• W2533156578 hasPublicationYear "2016" @default.
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