The Third Workshop on the Theory and Practice of Differential Privacy (TPDP 2017) seeks 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. Submissions will undergo a lightweight review process and will be judged on originality, relevance, interest and clarity. Submissions 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 as a talk or a poster.
The goal of TPDP is to stimulate the discussion on the relevance of differentially private data analyses in practice. Specific topics of interest at the workshop include (but are not limited to):
- theory of differential privacy
- privacy preserving machine learning
- differential privacy and statistics
- differential privacy and security
- differential privacy and data analysis
- trade-offs between privacy protection and analytic utility
- differential privacy and surveys
- programming languages for differential privacy
- relaxations of the differential privacy definition
- differential privacy vs other privacy notions and methods
- experimental studies using differential privacy
- differential privacy implementations
- differential privacy and policy-making
- applications of differential privacy
Important Dates
Abstract Submission due date: August 4th, 2017
Notification date: September 4th, 2017 (approximately)
Workshop date: October 30th, 2017
Organizing and Program Committee
Rachel Cummings - Caltech and Georgia Tech
Marco Gaboardi - University of Buffalo, SUNY
Justin Hsu - University of Pennsylvaia
Aleksandra Korolova - University of Southern California
Ashwin Machanavajjhala - Duke University
Gerome Miklau - UMass Amherst
Abhradeep Guha Thakurta - UC Santa Cruz
Jonathan Ullman (chair) - Northeastern University