Theory and Practice of Differential Privacy (TPDP) - Deadline Extension: August 11 2017

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

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