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Opening day
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Submission deadlineNo deadline
Differential privacy has become the pre-eminent framework to measure and limit loss in privacy when statistics about sensitive data are computed and released. The theoretical study of differential privacy has extended far beyond this scope, establishing deep relationships with long studied areas of theoretical computer science, such as learning theory, robust algorithm design, adaptive data analysis and hypothesis testing. The goal of this workshop is to share and disseminate recent developments in the theory of differential privacy. Researchers are encouraged to submit a 4 page extended abstract on the following topics:
1) New differentially private mechanisms for wide variety of algorithmic problems superior to prior work
2) Novel privacy accounting techniques and analyses
3) Lower bounds/impossibility results related to differential privacy
4) Relationships between differential privacy and other areas of TCS (for example formal methods)