M$$^2$$M: A General Method to Perform Various Data Analysis Tasks from a Differentially Private Sketch

Author:

Houssiau Florimond,Schellekens Vincent,Chatalic Antoine,Annamraju Shreyas Kumar,de Montjoye Yves-Alexandre

Publisher

Springer International Publishing

Reference38 articles.

1. Abowd, J.M.: The US census bureau adopts differential privacy. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, p. 2867 (2018)

2. Aktay, A., et al.: Google Covid-19 community mobility reports: anonymization process description (version 1.0). arXiv preprint arXiv:2004.04145 (2020)

3. Balog, M., Tolstikhin, I., Schölkopf, B.: Differentially private database release via kernel mean embeddings. In: International Conference on Machine Learning, pp. 414–422 (2018)

4. Barak, B., Chaudhuri, K., Dwork, C., Kale, S., McSherry, F., Talwar, K.: Privacy, accuracy, and consistency too: a holistic solution to contingency table release. In: Proceedings of the Twenty-Sixth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 273–282 (2007)

5. Barbaro, M., Zeller, T., Hansell, S.: A face is exposed for AOL searcher no. 4417749. New York Times 9(2008), 8For (2006)

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