The open-source release comes with another exciting paper we've been working on for a while now, about how you can bake differential privacy in an SQL engine at scale and in a usable way
Feedback is extremely encouraged as always!
https://arxiv.org/abs/1909.01917
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Finally, we explain this code & paper release in a shiny blog post that gives a few real-world examples of what it can do and how we use it for.https://developers.googleblog.com/2019/09/enabling-developers-and-organizations.html …
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I'll jot down more thoughts about this later today, highlighting particularly interesting tidbits of the paper, and explaining why I find the open-source incredibly exciting. Now I need to have lunch and let the excitement calm down a bit. Maybe. Not sure I'll manage :D
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OK, here's a thread with some more thoughts about the news, including chosen bits from our paper:https://twitter.com/TedOnPrivacy/status/1169607985540325377 …
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Addendum: this was a team effort! I'm not the main person behind this, I'm just the one who shitposts on Twitter most often, and I happened to be awake when the blog post was published ^^
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Credit goes to my amazing colleagues: Alain Forget,
@b_gipson,@CeliaZhang19, Daniel Simmons-Marengo, Ian Pudney, Jin Fu, Michael Daub,@miguelguevaraII, Priyanka Sehgal, Royce Wilson & William Lam
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Blessings on whoever chose this wonderfully findable project name.

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Honestly we tried to find a funny acronym or something but we weren't very inspired so we just went for simplicity ^^
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Great to see more open source software on differential privacy! Looking forward to seeing what this grows into. We
@IBMResearch are busy expanding our Python DP library, diffprivlib, with a focus on out-of-the-box DP for ML. Exciting times!Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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THIS IS SO COOL and also omg been dreaming about this
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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