THIS LECTURE HAS BEEN CANCELLED
Differential Privacy: The Mathematical Bulwark against Reidentification and Reconstruction
March 26, 2020 8:00 PM Jadwin A10
Traditional "anonymized" data, valuable though it is for research and planning, can often be attacked to break privacy quite easily, as was dramatically demonstrated in the "Netflix Challenge" some years ago. Differential Privacy is a rigorous technique for fixing this flaw using sophisticated mathematics. Differentially private systems provide both useful statistics to the well-intentioned data analyst and strong protection against arbitrarily powerful adversarial system users.
Differentially private systems "don't care" what the adversary knows, now or in the future. Finally, differentially private systems can rigorously bound and control the cumulative privacy loss that accrues over many interactions with the confidential data. These unique properties, together with the abundance of commercial data sources and the surprising ease with which they can be deployed by a privacy adversary, led the US Census Bureau to adopt differential privacy as the disclosure avoidance methodology of the 2020 decennial census. The technology is also widely deployed in industry. This talk will motivate and define differential privacy and discuss the significance of equal privacy for all.
THIS LECTURE IS FREE AND OPEN TO THE PUBLIC