Great work! Just reading your paper, I was thinking how one can formally tune \epsilon and \delta to get certain privacy/accuracy guarantees. Any thoughts?
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Each published algorithm typically comes with this - the part that is less well understood is what kind of accuracy/speed tradeoff you get with various tunings. Our framework is supposed to make such empirical works *much* easier.
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This tweet made my day! I'm in the process of creating a video on Privacy Matters for Data Science and this paper caught my eye. Thanks!
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We should chat! :)
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hey,
@ogirardot and@WiemZin presented something very similar at@ScalaIO_FR two weeks ago! See thread https://twitter.com/fanf42/status/1057589606303129600 … and link to slides:https://twitter.com/ogirardot/status/1057617973991608320 …Thanks. Twitter will use this to make your timeline better. UndoUndo
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why would **profit-driven** institutions democratize deep learning? Because DL is a black box with too many buttons for the masses to keep pushing until, *fingers-crossed*, something magical happens.
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Happy to have contributed a little bit with our code from
@coMindOrg. Keep up the good work!Thanks. Twitter will use this to make your timeline better. UndoUndo
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Very awesome! I've just finished studying differential privacy
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Sir you are making it look really easy for academic scholars like us....kudos
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@adversariel@david_madras seems up your alleyThanks. Twitter will use this to make your timeline better. UndoUndo
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I know that the design of differential privacy needs to include intended application, e.g., if privacy design includes geospatial resolution. Is there research in privacy of data aggregations as well as individual records? Application, e.g.https://www.bbc.com/news/technology-42853072 …
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