Private frequency estimation via projective geometry | Vidéo | Carmin.tv

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Private frequency estimation via projective geometry

By Jelani Nelson

Appears in collection : Learning and Optimization in Luminy - LOL2022 / Apprentissage et Optimisation à Luminy - LOL2022

Many of us use smartphones and rely on tools like auto-complete and spelling auto-correct to make using these devices more pleasant, but building these tools presents a challenge. On the one hand, the machine-learning algorithms used to provide these features require data to learn from, but on the other hand, who among us is willing to send a carbon copy of all our text messages to device manufacturers to provide that data? 'Local differential privacy' and related concepts have emerged as the gold standard model in which to analyze tradeoffs between losses in utility and privacy for solutions to such problems. In this talk, we give a new state-of-the-art algorithm for estimating histograms of user data, making use of projective geometry over finite fields coupled with a reconstruction algorithm based on dynamic programming. This talk is based on joint work with Vitaly Feldman (Apple), Huy Le Nguyen (Northeastern), and Kunal Talwar (Apple).

Information about the video

Date of recording 03/10/2022
Date of publication 10/11/2022
Institution CIRM
Licence CC BY NC ND
Language English
Audience Researchers, Graduate Students
Director(s) Guillaume Hennenfent
Format MP4

Citation data

DOI 10.24350/CIRM.V.19965703
Cite this video Nelson, Jelani (03/10/2022). Private frequency estimation via projective geometry. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19965703
URL https://dx.doi.org/10.24350/CIRM.V.19965703

Domain(s)

Cryptography and Security

Bibliography

FELDMAN, Vitaly, NELSON, Jelani, NGUYEN, Huy, et al. Private frequency estimation via projective geometry. In : International Conference on Machine Learning. PMLR, 2022. p. 6418-6433. - https://proceedings.mlr.press/v162/feldman22a.html

MSC codes

Document(s)

https://www.cirm-math.fr/RepOrga/2551/Slides/nelson_compressed.pdf

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