TPDP Will be Virtual

Due to the concerns for the health and safety of attendees, CCS and affiliated workshops will take place as virtual events.

Registration for TPDP can be done through the CCS website here: https://www.sigsac.org/ccs/CCS2020/registration.html.

Program

(all listed times are Eastern time)

10:00-10:15 Opening Remarks
10:15-11:00 OpenDP: A Community Effort to Build Trustworthy Differential Privacy Software
Salil Vadhan (Invited Speaker)
11:00-11:30 Break
11:30-12:15 Private Reinforcement Learning with PAC and Regret Guarantees
by Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy and Z. Steven Wu

Auditing Differentially Private Machine Learning: How Private is Private SGD?
by Matthew Jagielski, Jonathan Ullman and Alina Oprea

Characterizing Private Clipped Gradient Descent on Convex Generalized Linear Problems
by Shuang Song, Om Thakkar and Abhradeep Thakurta
12:15-1:00 Poster Session 1
1:00-2:00 Lunch Break
2:00-2:45 Private Query Release Assisted by Public Data
by Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan Ullman and Z. Steven Wu

An Equivalence Between Private Classification and Online Prediction
by Mark Bun, Roi Livni and Shay Moran

Differentially Private Set Union
by Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi and Sergey Yekhanin
2:45-3:30 Poster Session 2
3:30-4:00 Break
4:00-4:45 Invited Talk 2: Christina Ilvento
4:45-5:30 Poster Session 3

Posters

This year's TPDP posters will be divided into three poster sessions.

Poster Session 1:

Poster Session 2:

Poster Session 3:

Context

Differential privacy is a promising approach to privacy-preserving data analysis. Differential privacy provides strong worst-case guarantees about the harm that a user could suffer from participating in a differentially private data analysis, but is also flexible enough to allow for a wide variety of data analyses to be performed with a high degree of utility. Having already been the subject of a decade of intense scientific study, it has also now been deployed at government agencies such as the U.S. Census Bureau and companies including Apple, Google, Facebook, and Microsoft.

Researchers in differential privacy span many distinct research communities, including algorithms, computer security, cryptography, databases, data mining, machine learning, statistics, programming languages, social sciences, and law. This workshop will bring researchers from these communities together to discuss recent developments in both the theory and practice of differential privacy.

Specific topics of interest for the workshop include (but are not limited to):

Submission

The goal of TPDP is to stimulate the discussion on the relevance of differentially private data analyses in practice. For this reason, we seek contributions from different research areas of computer science and statistics.

Authors are invited to submit a short abstract (4 pages maximum) of their work.

Submissions will undergo a lightweight review process and will be judged on originality, relevance, interest and clarity. Submission should describe novel work or work that has already appeared elsewhere but that can stimulate the discussion between different communities at the workshop. Accepted abstracts will be presented at the workshop either as a talk or a poster.

The workshop will not have formal proceedings and is not intended to preclude later publication at another venue.

Selected papers from the workshop will be invited to submit a full version of their work for publication in a special issue of the Journal of Privacy and Confidentiality.

Call for Papers: pdf

Invited Speakers

Important Dates

Abstract Submission
June 21 (anywhere on earth)
June 28 (anywhere on earth)
Notification
August 8
Workshop
November 13

Organizing and Program Committee

Submission website


Easychair TPDP 2020