Boston Differential Privacy Summer School

Tentative Program

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.

Venue: The lectures will take place in room 101 of the Rajen Kilachand Center for Integrated Life Sciences and Engineering, 610 Commonwealth Ave, Boston, MA 02215. Breaks and lunches will be served in the atrium of Kilachand Center.

Covid Policy: All the students attending the summer school are recommended to wear a tight mask during class and when inside building. We also recommend to follow general covid related procedures. Students who develop symptoms during the school are recommended to quarantine following the BU covid policy that can be found here:
https://www.bu.edu/shs/covid-19/quarantine-isolation-guidance/

Schedule: Printable/readable Google Doc version

Day	8:00-9:00	9:00-10:20	10:20-11:00	11:00-12:20	12:20-1:30	1:30-2:30	2:30-2:50	2:50-3:50	4:10-5:30
Monday	Breakfast	Kobbi Nissim	Coffee Break	Kobbi Nissim	Lunch	Rachel Cummings	Coffee Break	Aloni Cohen	Steven Wu
Day	8:00-9:00	9:00-10:20	10:20-11:00	11:00-12:20	12:20-1:30	1:30-2:50	2:50-3:20	3:20-4:40	5:00-6:20
Tuesday	Breakfast	Dan Kifer	Coffee Break	Dan Sheldon	Lunch	FORC	FORC	FORC	FORC
Wednesday	Breakfast	Dan Sheldon	Coffee Break	Steven Wu	Lunch	Uri Stemmer	Coffee Break	Dan Kifer	Problem session
Thursday	Breakfast	Steven Wu	Coffee Break	Uri Stemmer	Lunch	Dan Kifer	Coffee Break	Dan Sheldon	Uri Stemmer
Friday	Breakfast	Uri Stemmer	Coffee Break	Dan Kifer	Lunch	Dan Sheldon	Coffee Break	Steven Wu	Problem session

Day

8:00-9:00

9:00-10:20

10:20-11:00

11:00-12:20

12:20-1:30

1:30-2:30

2:30-2:50

2:50-3:50

4:10-5:30

Monday

Breakfast

Kobbi Nissim

Coffee Break

Kobbi Nissim

Lunch

Rachel Cummings

Coffee Break

Aloni Cohen

Steven Wu

Day

8:00-9:00

9:00-10:20

10:20-11:00

11:00-12:20

12:20-1:30

1:30-2:50

2:50-3:20

3:20-4:40

5:00-6:20

Tuesday

Breakfast

Dan Kifer

Coffee Break

Dan Sheldon

Lunch

FORC

Wednesday

Breakfast

Dan Sheldon

Coffee Break

Steven Wu

Lunch

Uri Stemmer

Coffee Break

Dan Kifer

Problem session

Thursday

Breakfast

Steven Wu

Coffee Break

Uri Stemmer

Lunch

Dan Kifer

Coffee Break

Dan Sheldon

Uri Stemmer

Friday

Breakfast

Uri Stemmer

Coffee Break

Dan Kifer

Lunch

Dan Sheldon

Coffee Break

Steven Wu

Problem session

Courses topics:

Dan Kifer - Differential Privacy Issues in Practice

Dan Sheldon - Differential Privacy and Statistical Inference

Uri Stemmer - Differential privacy without a central database

Steven Wu - A gentle introduction to private synthetic data generation

Kobbi Nissim - A short introduction to differential privacy

Rachel Cummings - Explaining differential privacy to non-experts

Aloni Cohen - Differential privacy laws

Note: From Monday through Wednesday the Symposium on Foundations of Responsible Computing (FORC) will be held in Cambridge, MA. The program includes several works on differential privacy. FORC is free to attend (as long as you register) and has a poster session on Tuesday afternoon. Tuesday afternoon was intentionally freed up in the summer school schedule to allow attendees to go to FORC if they like. Please consider presenting some of your work!

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

Registration will cover breakfast, 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.

Registration is now closed.

Differential Privacy

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.

Logistics

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.

Participating

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.