Dear all,
There will be a noon seminar next Thursday, June 20, given by Jakub Tětek, our former bachelor student, now finishing PhD in Copenhagen. Please find the talk details below.
I plan to go for lunch with Jakub before the talk (at about 11:20) -- let me know if you'd like to join us. Likewise, if you'd like to meet with Jakub, I can arrange that. He's visiting me till the end of June.
Best regards, Pavel Veselý
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Better Differentially Private Approximate Histograms and Heavy Hitters using the Misra-Gries Sketch Jakub Tětek (University of Copenhagen) June 20, 2024, 12:20 in S6
Abstract: We consider the problem of computing differentially private approximate histograms and heavy hitters in a stream of elements. In the non-private setting, this is often done using the sketch of Misra and Gries [Science of Computer Programming, 1982]. Chan, Li, Shi, and Xu [PETS 2012] describe a differentially private version of the Misra-Gries sketch, but the amount of noise it adds can be large and scales linearly with the size of the sketch; the more accurate the sketch is, the more noise this approach has to add. We present a better mechanism for releasing a Misra-Gries sketch under $(\varepsilon,\delta)$-differential privacy. It adds noise with magnitude independent of the size of the sketch; in fact, the maximum error coming from the noise is the same as the best known in the private non-streaming setting, up to a constant factor. Our mechanism is simple and likely to be practical. In the full version of the paper we also give a simple post-processing step of the Misra-Gries sketch that does not increase the worst-case error guarantee. It is sufficient to add noise to this new sketch with less than twice the magnitude of the non-streaming setting. This improves on the previous result for $\varepsilon$-differential privacy where the noise scales linearly to the size of the sketch.