Theoretical Computer Science Spring School: Machine Learning / Ecole de Printemps d'Informatique Théorique : Apprentissage Automatique

Collection Theoretical Computer Science Spring School: Machine Learning / Ecole de Printemps d'Informatique Théorique : Apprentissage Automatique

Organizer(s) Cappé, Olivier ; Garivier, Aurélien ; Gribonval, Rémi ; Kaufmann, Emilie ; Vernade, Claire

Date(s) 23/05/2022 - 27/05/2022

linked URL https://conferences.cirm-math.fr/2542.html

00:00:00 / 00:00:00

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Privacy in machine learning

By Rachel Cummings

differential privacy

Privacy concerns are becoming a major obstacle to using data in the way that we want. It's often unclear how current regulations should translate into technology, and the changing legal landscape surrounding privacy can cause valuable data to go unused. How can data scientists make use of potentially sensitive data, while providing rigorous privacy guarantees to the individuals who provided data? A growing literature on differential privacy has emerged in the last decade to address some of these concerns. Differential privacy is a parameterized notion of database privacy that gives a mathematically rigorous worst-case bound on the maximum amount of information that can be learned about any one individual's data from the output of a computation. Differential privacy ensures that if a single entry in the database were to be changed, then the algorithm would still have approximately the same distribution over outputs. In this talk, we will see the definition and properties of differential privacy; survey a theoretical toolbox of differentially private algorithms that come with a strong accuracy guarantee; and discuss recent applications of differential privacy in major technology companies and government organizations.

Information about the video

Date of recording 24/05/2022
Date of publication 21/06/2022
Institution CIRM
Licence CC BY NC ND
Language English
Audience Researchers, Graduate Students
Director(s) Jean Petit
Format MP4

Citation data

DOI 10.24350/CIRM.V.19921503
Cite this video Cummings, Rachel (24/05/2022). Privacy in machine learning. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19921503
URL https://dx.doi.org/10.24350/CIRM.V.19921503

Domain(s)

Bibliography

Dwork, C., McSherry, F., Nissim, K., Smith, A. (2006). Calibrating Noise to Sensitivity in Private Data Analysis. In: Halevi, S., Rabin, T. (eds) Theory of Cryptography. TCC 2006. Lecture Notes in Computer Science, vol 3876. Springer, Berlin, Heidelberg - https://doi.org/10.1007/11681878_14
Cynthia Dwork and Aaron Roth. 2014. The Algorithmic Foundations of Differential Privacy. Found. Trends Theor. Comput. Sci. 9, 3–4 (August 2014), 211–407 - https://doi.org/10.1561/0400000042
F. McSherry and K. Talwar, "Mechanism Design via Differential Privacy," 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07), 2007, pp. 94-103 - https://doi.org/10.1109/FOCS.2007.66

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