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Protecting Privacy with MATH (Collab with the Census)

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This video was made in collaboration with the US Census Bureau and fact-checked by Census Bureau scientists. Any opinions and errors are my own. For more information, visit https://census.gov/about/policies/privacy/statistical_safeguards.html or search “differential privacy” at http://census.gov.

REFERENCES
Differential Privacy in the Wild: http://www.vldb.org/pvldb/vol9/p1611-machanavajjhala.pdf

Harvard University Privacy Tools Project: https://privacytools.seas.harvard.edu/differential-privacy

Simons Institute Workshop Video Recordings and Articles Archive: https://simons.berkeley.edu/workshops/schedule/6281

Cynthia Dwork (key inventor of Differential Privacy), giving a great intro talk about differential privacy: https://www.youtube.com/watch?v=lg-VhHlztqo

Shiva P Kasiviswanathan and Adam Smith. On the semantics of differential privacy: A Bayesian formulation. Journal of Privacy and Confidentiality, 6(1):1–16, 2014.

Shiva Prasad Kasiviswanathan and Adam Smith. On the ‘semantics’ of differential privacy: A Bayesian formulation. 2015. https://arxiv.org/abs/0803.3946

Daniel Kifer and Ashwin Machanavajjhala. A rigorous and customizable framework for privacy. In ACM Symposium on Principles of Database Systems (PODS), 2012.

Daniel Kifer and Ashwin Machanavajjhala. Pufferfish: A framework for mathematical privacy definitions. ACM Trans. Database Syst., 39(1):3, 2014.

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