[ietf-privacy] CfP: INFER 2016 - Deadline Extended

Fatemeh Shirazi <fatemeh.shirazi@esat.kuleuven.be> Tue, 17 May 2016 11:15 UTC

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Subject: [ietf-privacy] CfP: INFER 2016 - Deadline Extended
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(Apologies for multiple copies)


Infer 2016: International Workshop on Inference and Privacy in a
Hyperconnected World

July 18, 2016 Darmstadt, Germany


Motivation and Scope


The fields of embedded computing, wireless communication, data mining
and artificial intelligence are exhibiting impressive improvements.
Their combination fosters the emergence of "smart environments": Systems
made of networked physical objects embedded in public places and private
spheres of everyday individuals. This trend is supporting the rise of a
broad variety of data-driven services that are highly customized to
various aspect of our life, and hold great social and economic
potential. Examples include wearable computing systems and applications
for monitoring of personal health and physical/social activities;
Intelligent Transport Systems (ITS) relying on cars that are becoming
increasingly aware of their environment and drivers; and home automation
systems that even support face and emotion recognition applications and
provide Web access to entirely novel types of content. Such disruptive
technologies are expected to increasingly rely on sophisticated machine
learning and statistical inference techniques to obtain a much clearer
semantic understanding of people’ states, activities, environments,
contexts and goals. However, these developments also raise new
technical, social, ethical and legal privacy challenges which, if left
unaddressed, will jeopardize the wider deployment and thus undermine
potential social and economic benefits of the aforementioned emerging
technologies. Indeed, algorithms increasingly used for complex
information processing in today's hyper-connected society are rarely
designed with privacy and data protection in mind. On the other hand,
privacy researchers are increasingly interested in leveraging machine
learning and inference models when designing both attacks and innovative
privacy-enhancing tools. Aiming to foster an exchange of ideas and an
interdisciplinary discussion on both theoretical and practical issues
that applying inference models to jeopardize/enhance data protection and
privacy may entail, this workshop provides researchers and practitioners
with a unique opportunity to share their perspectives with others
interested in the various aspects of privacy and inference. Topics of
interest include (but are not limited to):

# Adversarial learning and emerging privacy threats
# Anonymous communication
# Discrimination-aware Learning
# Privacy-preserving deep learning models
# Deep learning models for privacy
# Privacy-preserving clustering, ranking, regression, etc.
# Privacy and anonymity metrics
# Statistical disclosure control
# Differential privacy and relaxations
# Machine learning and statistical inference on encrypted data
# Machine learning and statistical inference for cybersecurity (e.g.,
for malware and misbehaviour detection, analysis, prevention)
# Social graph matching and de-anonymization techniques
# Private information retrieval
# Algorithms and accountability
# Case studies and experimental datasets
# Legal, regulatory, and ethical issues
# …

Important Dates


Paper Submission deadline: June 1, 11:59pm PST, 2016 [extended]

Notification: June 27, 2016

Camera ready: July 10, 2016

Workshop: July 18, 2016



The workshop seeks to bring together experts and practitioners from
academia, industry and government to discuss open research problems,
case studies, and legal and policy issues related to inference and
privacy. Authors are invited to submit either:

# Full research papers that present relatively mature research results
on topics related to data analysis /statistical inference and
privacy/data protection;
# Short papers that discuss new attacks and inspiring visions for
countermeasures, or present interdisciplinary research related to case
studies and legal and policy issues; or
# Industry papers that share practical experiences.

Papers must be written in English. Authors are required to follow LNCS
guidelines. The length of the full paper (in the proceedings format)
must not exceed 20 pages, including the bibliography and well-marked
appendices. Short papers and industry papers must not exceed 9 pages. PC
members are not required to read the appendices, and so the paper should
be intelligible without them.

Papers are to be submitted electronically and in pdf format only using
the EasyChair conference management system

It is planned to publish revised selected papers as a post-proceedings
volume in Springer Verlag’s LNCS series (final approval pending).

Program Committee Chairs


Michael Waidner, Fraunhofer SIT / TU Darmstadt, Germany
Thorsten Strufe, TU Dresden, Germany
Amir Herzberg, Bar Ilan University, Israel
Hervais Simo, Fraunhofer SIT, Germany

Program Committees
Rafael Accorsi, PWC, Switzerland
Nikita Borisov, University of Illinois at Urbana-Champaign, USA
Ulf Brefeld, Leuphana University Lüneburg, Germany
Michael Brückner, Amazon, Germany
Yves-Alexandre de Montjoye, MIT, USA
Shlomi Dolev, Ben-Gurion University, Israel
Tariq Elahi, KU Leuven, Belgium
Simone Fischer-Hübner, Karlstad University, Sweden
Marit Hansen, ULD, Germany
Stratis Ioannidis, Northeastern University, USA
Aaron D. Jaggard, U.S. Naval Research Laboratory, USA
Frederik Janssen, Technische Universität Darmstadt, Germany
Anja Lehmann, IBM Research Zürich, Switzerland
Daniel Le Métayer, INRIA, France
Tobias Matzner, University of Tübingen, Germany
Helen Nissenbaum, New York University, USA
Stefan Schiffner, ENISA, Greece
Haya Shulman, Fraunhofer SIT, Germany

Publicity Co-chairs
Fatemeh Shirazi, KU Leuven, Belgium
Christian Zimmermann, University of Freiburg, Germany