The school will start on Monday June 6 and end on Friday June 10,
2022. The first day we will have several lectures aimed at providing
all the participants with a common vocabulary and at introducing
participants to several related areas where differential privacy is
having an impact. The rest of the days we will have four courses which will provide a deep
dive on four different foundational topics in differential privacy.
The tentative program is as follows:
|Monday||Kobbi Nissim||Coffee Break||Kobbi Nissim||Lunch||Rachel Cummings||Coffee Break||Aloni Cohen|
|Tuesday||Dan Kifer||Coffee Break||Dan Sheldon||Lunch||Steven Wu||Coffee Break||Uri Stemmer||Problem session|
|Wednesday||Dan Sheldon||Coffee Break||Steven Wu||Lunch||Uri Stemmer||Coffee Break||Dan Kifer||Problem session|
|Thursday||Steven Wu||Coffee Break||Uri Stemmer||Lunch||Dan Kifer||Coffee Break||Dan Sheldon||Problem session|
|Friday||Uri Stemmer||Coffee Break||Dan Kifer||Lunch||Dan Sheldon||Coffee Break||Steven Wu||Problem session|
We hope that the school will also be an occasion for participants to socialize with the lecturers, organizers and the other participants. Socializing event are being planned and will be added to the schedule.
Registration will cover coffee breaks and lunches. It will not cover lodging and travel. Some dorm-style rooms on Boston University campus will be available for interested participants.
Differential privacy is a strong formal notion of data privacy which is currently used in several applications in industry and in the public sector. Differential privacy data analyses provide a strong statistical guarantee on the increase in harm that individuals can incur as a result of participating in the analyses.
Differential privacy can be integrated with methods and tools from different areas of computer science and statistics,such as machine learning, databases, security, etc. Several mechanisms have been developed to ensure differential privacy for a wide range of statistical and data analysis tasks. These mechanism have been integrated in several software tools.
The participants in the summer school will have the opportunity to learn about recent developments in the theory and practice of differential privacy.
We aim at having the school as an in person only event held on Boston University campus in Boston. We will monitor the status of the Covid-19 pandemic and if needed we will move the event to virtual.
More information will be added soon.
We welcome students who are interested in learning about the recent developments in differential privacy. In particular, the school will be aimed at advanced graduate students and scientists from statistical agencies, research labs and industry. We will assume that participants will have a basic knowledge of differential privacy and some of the basic mechanisms that can be used to guarantee it.