[TYPES/announce] Theory and Practice of Differential Privacy (TPDP): Deadline Extension

[Justin Hsu]

CALL FOR PAPERS (Extended Deadline)
TPDP 2017
Third Workshop on the Theory and Practice of Differential Privacy
October 30th 2017, Dallas, TX, USA
Affiliated with CCS 2017
Website: http://tpdp.cse.buffalo.edu/2017/

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.


** Invited Speakers **

Dan Kifer - Pennsylvania State University

(Others to be announced)


** Important Dates **

Submission --- August 11th, 2017 *Extended* (Anywhere on Earth)
Notification --- September 4th, 2017
Workshop --- October 30, 2017


** Submissions **

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 (2-4 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 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.


** Topics **

Specific topics of interest for 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


** 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
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