9:00 - 9:10
9:10 - 10:00

Composition, Verification, and Differential Privacy
Justin Hsu (Invited Speaker)

10:00 - 10:45

Coffee Break
10:45 - 11:35
Deploying Differential Privacy for Learning on Sensitive Data
Úlfar Erlingsson (Invited Speaker)
11:35 - 11:55

Local Differential Privacy for Evolving Data
Matthew Joseph, Aaron Roth, Jonathan Ullman and Bo Waggoner

11:55 - 2:00

Lunch Break
2:00 - 3:00

Private PAC learning implies finite Littlestone dimension
Noga Alon, Roi Livni, Maryanthe Malliaris and Shay Moran

Linear Program Reconstruction in Practice
Aloni Cohen and Kobbi Nissim

Optimizing error of high­ dimensional statistical queries under differential privacy
Ryan McKenna, Gerome Miklau, Michael Hay and Ashwin Machanavajjhala

3:00 - 3:45

Coffee Break
3:45 - 4:35

PSI: A Private Data-Sharing Interface
Salil Vadhan (Invited Speaker)
4:35 - 4:55

Towards Modeling Singling Out
Aloni Cohen and Kobbi Nissim


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. You can find the deadline and submission server link on the right.

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):

Call for Papers: pdf

Invited Speakers

Important Dates

Abstract Submission
July 20 (anywhere on earth)
July 27 (anywhere on earth)
August 13
August 18
October 15

Submission website

Easychair TPDP 2018

Organizing and Program Committee