Program (Dallas Ballroom D3)

8:45 - 9:00
9:00 - 9:45

Challenges of Differential Privacy in Practice
Dan Kifer - Pennsylvania State University (Invited Speaker)
9:45 - 10:00

Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM
Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, and Zhiwei Steven Wu
10:00 - 10:45
Coffee Break
10:45 - 11:30

Privacy with Constraints: Challenges & Opportunities
Xi He - Duke University (Invited Speaker)
11:30 - 12:00

Ektelo: A Framework for Defining Differentially-Private Computations
Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Gerome Miklau, Michael Hay, and Ashwin Machanavajjhala
Finite Sample Differentially Private Confidence Intervals
Vishesh Karwa and Salil Vadhan
12:00 - 2:00 Lunch Break
2:00 - 2:45

Modular Approach to Differential Privacy
Ilya Mironov - Google (Invited Speaker)
2:45 - 3:00

Practical Locally Private Heavy Hitters
Raef Bassily, Kobbi Nissim, Uri Stemmer and Abhradeep Thakurta
3:00 - 3:45
Coffee Break
3:45 - 4:30

Concentrating on the Foundations of Differential Privacy
Thomas Steinke - IBM Research (Invited Speaker)
4:30 - 5:00

BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model
Brendan Avent, Aleksandra Korolova, David Zeber, Torgeir Hovden and Benjamin Livshits
Differential Privacy on Finite Computers
Victor Balcer and Salil Vadhan
5:00 - 6:00
Poster Session



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 in products at government agencies such as the U.S. Census Bureau and companies like Apple and Google.

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.


The overall 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 (2-4 pages maximum) of their work. Abstracts must be written in English and do not need to be anonymous.

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

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

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

Invited Speakers

Important Dates

Abstract Submission
August 4, 2017
August 11, 2017 extended
(Anywhere on Earth)
September 4, 2017
October 30, 2017

Submission website

Easychair TPDP 2017

Organizing and Program Committee