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CALL FOR PAPERS
**********************************************************************
Infer 2016: International Workshop on Inference and Privacy in a =
Hyperconnected World
July 18, 2016 Darmstadt, Germany
https://www.sit.fraunhofer.de/en/infer2016/
**********************************************************************
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=92 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
# =85
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
Submission
-------------
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:
=B7 # 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 =
(https://www.easychair.org/conferences/?conf=3Dinfer2016).
It is planned to publish revised selected papers as a post-proceedings =
volume in Springer Verlag=92s 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 L=FCneburg, Germany
Michael Br=FCckner, Amazon, Germany
Yves-Alexandre de Montjoye, MIT, USA
Shlomi Dolev, Ben-Gurion University, Israel
Tariq Elahi, KU Leuven, Belgium
Simone Fischer-H=FCbner, Karlstad University, Sweden
Marit Hansen, ULD, Germany
Stratis Ioannidis, Northeastern University, USA
Aaron D. Jaggard, U.S. Naval Research Laboratory, USA
Frederik Janssen, Technische Universit=E4t Darmstadt, Germany
Anja Lehmann, IBM Research Z=FCrich, Switzerland
Daniel Le M=E9tayer, INRIA, France
Tobias Matzner, University of T=FCbingen, 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
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<html><body style=3D"word-wrap: break-word; -webkit-nbsp-mode: space; =
-webkit-line-break: after-white-space; "><p style=3D"margin: 0px 0px =
10px; font-size: 16px; font-family: Corbel; "><b>CALL FOR =
PAPERS</b></p><p style=3D"margin: 0px 0px 10px; font-size: 16px; =
font-family: Corbel; =
">**********************************************************************<b=
r>
<b>Infer 2016: International Workshop on Inference and Privacy in a =
Hyperconnected World</b></p><p style=3D"margin: 0px 0px 10px; font-size: =
16px; font-family: Corbel; ">July 18, 2016 Darmstadt, Germany</p><p =
style=3D"margin: 0px 0px 10px; font-size: 16px; font-family: Corbel; =
"><span style=3D"text-decoration: underline ; color: #4787ff"><a =
href=3D"https://www.sit.fraunhofer.de/en/infer2016/">https://www.sit.fraun=
hofer.de/en/infer2016/</a></span> <br>
=
**********************************************************************</p>=
<p style=3D"margin: 0px 0px 10px; font-size: 16px; font-family: Corbel; =
"><b><br>
Motivation and Scope</b></p><p style=3D"margin: 0px 0px 10px; font-size: =
16px; font-family: Corbel; "><b>-------------</b></p><p style=3D"margin: =
0px 0px 10px; font-size: 16px; font-family: Corbel; ">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=92 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):</p><div style=3D"margin: 0px; =
font-size: 16px; font-family: Corbel; "><span style=3D"font-size: 12px; =
"># </span>Adversarial learning and emerging privacy =
threats </div><div style=3D"margin: 0px; font-size: 16px; =
font-family: Corbel; "><span style=3D"font-size: 12px; =
"># </span>Anonymous communication </div><div =
style=3D"margin: 0px; font-size: 16px; font-family: Corbel; "><span =
style=3D"font-size: 12px; "># =
</span>Discrimination-aware Learning </div><div style=3D"margin: =
0px; font-size: 16px; font-family: Corbel; "><span style=3D"font-size: =
12px; "># </span>Privacy-preserving deep =
learning models </div><div style=3D"margin: 0px; font-size: 16px; =
font-family: Corbel; "><span style=3D"font-size: 12px; =
"># </span>Deep learning models for =
privacy </div><div style=3D"margin: 0px; font-size: 16px; =
font-family: Corbel; "><span style=3D"font-size: 12px; =
"># </span>Privacy-preserving clustering, ranking, =
regression, etc. </div><div style=3D"margin: 0px; font-size: 16px; =
font-family: Corbel; "><span style=3D"font-size: 12px; =
"># </span>Privacy and anonymity =
metrics </div><div style=3D"margin: 0px; font-size: 16px; =
font-family: Corbel; "><span style=3D"font-size: 12px; =
"># </span>Statistical disclosure =
control </div><div style=3D"margin: 0px; font-size: 16px; =
font-family: Corbel; "><span style=3D"font-size: 12px; =
"># </span>Differential privacy and =
relaxations </div><div style=3D"margin: 0px; font-size: 16px; =
font-family: Corbel; "><span style=3D"font-size: 12px; =
"># </span>Machine learning and statistical inference =
on encrypted data </div><div style=3D"margin: 0px; font-size: 16px; =
font-family: Corbel; "><span style=3D"font-size: 12px; =
"># </span>Machine learning and statistical inference =
for cybersecurity (e.g., for malware and misbehaviour detection, =
analysis, prevention) </div><div style=3D"margin: 0px; font-size: =
16px; font-family: Corbel; "><span style=3D"font-size: 12px; =
"># </span>Social graph matching and de-anonymization =
techniques </div><div style=3D"margin: 0px; font-size: 16px; =
font-family: Corbel; "><span style=3D"font-size: 12px; =
"># </span>Private information =
retrieval </div><div style=3D"margin: 0px; font-size: 16px; =
font-family: Corbel; "><span style=3D"font-size: 12px; =
"># </span>Algorithms and =
accountability </div><div style=3D"margin: 0px; font-size: 16px; =
font-family: Corbel; "><span style=3D"font-size: 12px; =
"># </span>Case studies and experimental =
datasets </div><div style=3D"margin: 0px; font-size: 16px; =
font-family: Corbel; "><span style=3D"font-size: 12px; =
"># </span>Legal, regulatory, and ethical =
issues </div><div style=3D"margin: 0px; font-size: 12px; =
font-family: Corbel; "># <span style=3D"font-size: =
16px; ">=85 </span></div><div style=3D"margin: 0px; font-size: =
12px; font-family: Corbel; min-height: 14px; "><br></div><p =
style=3D"margin: 0px 0px 10px; font-size: 16px; font-family: Corbel; =
"><b>Important Dates</b></p><p style=3D"margin: 0px 0px 10px; font-size: =
16px; font-family: Corbel; "><b>-------------</b></p><p style=3D"margin: =
0px 0px 10px; font-size: 16px; font-family: Corbel; ">Paper Submission =
deadline: June 1, 11:59pm PST, 2016 [extended]</p><p style=3D"margin: =
0px 0px 10px; font-size: 16px; font-family: Corbel; ">Notification: June =
27, 2016</p><p style=3D"margin: 0px 0px 10px; font-size: 16px; =
font-family: Corbel; ">Camera ready: July 10, 2016</p><p style=3D"margin: =
0px 0px 10px; font-size: 16px; font-family: Corbel; ">Workshop: July 18, =
2016</p><div style=3D"margin: 0px 0px 10px; font-size: 16px; =
font-family: Corbel; "> <br =
class=3D"webkit-block-placeholder"></div><p style=3D"margin: 0px 0px =
10px; font-size: 16px; font-family: Corbel; "><b>Submission</b></p><p =
style=3D"margin: 0px 0px 10px; font-size: 16px; font-family: Corbel; =
"><b>-------------</b></p><p style=3D"margin: 0px 0px 10px; font-size: =
16px; font-family: Corbel; ">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:</p><p style=3D"margin: 0px 0px 12px; text-indent: -24px; =
font-size: 16px; font-family: Corbel; ">=B7<span style=3D"font-size: =
9px; "> =
</span># Full research papers that present relatively =
mature research results on topics related to data analysis /statistical =
inference and privacy/data protection; <br>
# 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 <br>
# Industry papers that share practical =
experiences.</p><p style=3D"margin: 0px 0px 10px; font-size: 16px; =
font-family: Corbel; ">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.</p><p style=3D"margin: =
0px 0px 10px; font-size: 16px; font-family: Corbel; ">Papers are to be =
submitted electronically and in pdf format only using the EasyChair =
conference management system (<a =
href=3D"https://www.easychair.org/conferences/?conf=3Dinfer2016">https://w=
ww.easychair.org/conferences/?conf=3Dinfer2016</a>).</p><p =
style=3D"margin: 0px 0px 10px; font-size: 16px; font-family: Corbel; =
">It is planned to publish revised selected papers as a post-proceedings =
volume in Springer Verlag=92s LNCS series (final approval =
pending).</p><div style=3D"margin: 0px; font-size: 12px; font-family: =
Corbel; min-height: 14px; "><br></div><p style=3D"margin: 0px 0px 10px; =
font-size: 16px; font-family: Corbel; "><b>Program Committee =
Chairs</b></p><p style=3D"margin: 0px 0px 10px; font-size: 16px; =
font-family: Corbel; "><b>-------------</b></p><p style=3D"margin: 0px =
0px 10px; font-size: 16px; font-family: Corbel; ">Michael Waidner, =
Fraunhofer SIT / TU Darmstadt, Germany</p><p style=3D"margin: 0px 0px =
10px; font-size: 16px; font-family: Corbel; ">Thorsten Strufe, TU =
Dresden, Germany</p><p style=3D"margin: 0px 0px 10px; font-size: 16px; =
font-family: Corbel; ">Amir Herzberg, Bar Ilan University, Israel</p><p =
style=3D"margin: 0px 0px 10px; font-size: 16px; font-family: Corbel; =
">Hervais Simo, Fraunhofer SIT, Germany</p><div style=3D"margin: 0px 0px =
10px; font-size: 16px; font-family: Corbel; "> <br =
class=3D"webkit-block-placeholder"></div><p style=3D"margin: 0px 0px =
10px; font-size: 16px; font-family: Corbel; "><b>Program =
Committees</b></p><p style=3D"margin: 0px 0px 10px; font-size: 16px; =
font-family: Corbel; "><b>-------------</b></p><p style=3D"margin: 0px =
0px 10px; font-size: 16px; font-family: Corbel; ">Rafael Accorsi, PWC, =
Switzerland</p><p style=3D"margin: 0px 0px 10px; font-size: 16px; =
font-family: Corbel; ">Nikita Borisov, University of Illinois at =
Urbana-Champaign, USA</p><p style=3D"margin: 0px 0px 10px; font-size: =
16px; font-family: Corbel; ">Ulf Brefeld, Leuphana University L=FCneburg, =
Germany</p><p style=3D"margin: 0px 0px 10px; font-size: 16px; =
font-family: Corbel; ">Michael Br=FCckner, Amazon, Germany</p><p =
style=3D"margin: 0px 0px 10px; font-size: 16px; font-family: Corbel; =
">Yves-Alexandre de Montjoye, MIT, USA</p><p style=3D"margin: 0px 0px =
10px; font-size: 16px; font-family: Corbel; ">Shlomi Dolev, Ben-Gurion =
University, Israel</p><p style=3D"margin: 0px 0px 10px; font-size: 16px; =
font-family: Corbel; ">Tariq Elahi, KU Leuven, Belgium</p><p =
style=3D"margin: 0px 0px 10px; font-size: 16px; font-family: Corbel; =
">Simone Fischer-H=FCbner, Karlstad University, Sweden</p><p =
style=3D"margin: 0px 0px 10px; font-size: 16px; font-family: Corbel; =
">Marit Hansen, ULD, Germany</p><p style=3D"margin: 0px 0px 10px; =
font-size: 16px; font-family: Corbel; ">Stratis Ioannidis, Northeastern =
University, USA</p><p style=3D"margin: 0px 0px 10px; font-size: 16px; =
font-family: Corbel; ">Aaron D. Jaggard, U.S. Naval Research Laboratory, =
USA</p><p style=3D"margin: 0px 0px 10px; font-size: 16px; font-family: =
Corbel; ">Frederik Janssen, Technische Universit=E4t Darmstadt, =
Germany</p><p style=3D"margin: 0px 0px 10px; font-size: 16px; =
font-family: Corbel; ">Anja Lehmann, IBM Research Z=FCrich, =
Switzerland</p><p style=3D"margin: 0px 0px 10px; font-size: 16px; =
font-family: Corbel; ">Daniel Le M=E9tayer, INRIA, France</p><p =
style=3D"margin: 0px 0px 10px; font-size: 16px; font-family: Corbel; =
">Tobias Matzner, University of T=FCbingen, Germany</p><p style=3D"margin:=
0px 0px 10px; font-size: 16px; font-family: Corbel; ">Helen Nissenbaum, =
New York University, USA</p><p style=3D"margin: 0px 0px 10px; font-size: =
16px; font-family: Corbel; ">Stefan Schiffner, ENISA, Greece</p><p =
style=3D"margin: 0px 0px 10px; font-size: 16px; font-family: Corbel; =
">Haya Shulman, Fraunhofer SIT, Germany</p><p style=3D"margin: 0px 0px =
10px; font-size: 16px; font-family: Corbel; "><b> </b></p><p =
style=3D"margin: 0px 0px 10px; font-size: 16px; font-family: Corbel; =
"><b>Publicity Co-chairs</b></p><p style=3D"margin: 0px 0px 10px; =
font-size: 16px; font-family: Corbel; "><b>-------------</b></p><p =
style=3D"margin: 0px 0px 10px; font-size: 16px; font-family: Corbel; =
">Fatemeh Shirazi, KU Leuven, Belgium</p><p style=3D"margin: 0px 0px =
10px; font-size: 16px; font-family: Corbel; ">Christian Zimmermann, =
University of Freiburg, Germany</p><div><br></div></body></html>=
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