UB - University at Buffalo, The State University of New York Computer Science and Engineering

CSE 711: Topics in Differential Privacy

This page refers to the Spring 2017 offering of CSE 711 only. The information on this page does not necessarily apply to every offering of CSE 711.

Spring 2017

21288

Marco Gaboardi

This will be a graduate level seminar-style course introducing differential privacy and some of its applications. Differential privacy is a promising approach to the privacy-preserving release of data: it offers a strong guaranteed bound on the increase in harm that a user incurs as a result of participating in a differentially private data analysis. Several mechanisms and software tools have been developed to ensure differential privacy for a wide range of data analysis tasks, such as combinat

This will be seminar-style course where each student will present part of the material on differential privacy and applications. The class will be based on the book The Algorithmic Foundations of Differential Privacy. Students are expected to read and comment the presented material previous to class by using NB. Every student will also be invited to engage on a project and to present the results at the end of the course. Discussion about all the aspects of the course will also take place on Piazza.

None presently required.

Ph.D.: None.

M.S.: This course does not fulfill core area (depth) or core course (breadth) requirements.

CSE 711: Topics in Differential Privacy

This page refers to the Spring 2017 offering of CSE 711 only. The information on this page does not necessarily apply to every offering of CSE 711.

Spring 2017

21288

Marco Gaboardi

This will be a graduate level seminar-style course introducing differential privacy and some of its applications. Differential privacy is a promising approach to the privacy-preserving release of data: it offers a strong guaranteed bound on the increase in harm that a user incurs as a result of participating in a differentially private data analysis. Several mechanisms and software tools have been developed to ensure differential privacy for a wide range of data analysis tasks, such as combinat

This will be seminar-style course where each student will present part of the material on differential privacy and applications. The class will be based on the book The Algorithmic Foundations of Differential Privacy. Students are expected to read and comment the presented material previous to class by using NB. Every student will also be invited to engage on a project and to present the results at the end of the course. Discussion about all the aspects of the course will also take place on Piazza.

None presently required.

Ph.D.: None.

M.S.: This course does not fulfill core area (depth) or core course (breadth) requirements.

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