full-disclosure - [FD] call for paper(information retrieval, privacy)

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Date: Fri, 22 May 2015 00:12:34 -0400
From: Hongkai Wu <hw271@...rgetown.edu>
To: senseval-discuss@...tserv.hum.gu.se, sentimentai@...oogroups.com, 
	siggen-list@...web.org, syntax@...oogroups.com,
	textanalytics@...oogroups.com, 
	fulldisclosure@...lists.org, cipher-cfp@...e-security.org,
	craemer@...c.org, dev@...p.org
Subject: [FD] call for paper(information retrieval, privacy)

Workshop on Privacy-Preserving Information Retrieval, held in conjunction
with the ACM SIGIR conference (August 13, 2015; Santiago de Chile)

Submission Deadline: June 5, 2015.
Acceptance Notifications: June 15, 2015
Camera-ready Deadline: June 22, 2015
Workshop: August 13, 2015
Submission types: Long papers (max. 4 pages in ACM SIG format),
Position papers (max. 2 pages in ACM SIG format)

Workshop format: Keynote speech, paper presentations, poster and group
discussions.

More information on: http://privacypreservingir.org

We look forward to your ideas and solutions to the cross-discipline
research on privacy and information retrieval.  The submissions should be
abount but not limited to the following research areas:
    - Privacy-related information retrieval models
    - Privacy in social media, micro blog, and people search
    - Evaluation for privacy-preserving IR
    - Leak of sensitive information in natural languages
    - Privacy in location-based services, recommender systems, and other IR
works on mobile app
    - Privacy preserving IR work for healthcare and other domains.

Potential topics for group discussion:
   - Protecting User Privacy in Search, Recommendation and Beyond: much
damage can be caused as users can be identified in AOL query log data and
Neflix log data, it is important to develop effective and efficient
solutions to protect users' privacy in information retrieval applications.
    - Dataset Distribution and Evaluation: How does privacy affect IR test
dataset distribution and evaluation? Forinstance, web query logs and
medical records could not be shared without privacy concerns to the public
or the researchers. How to anonymize the datasets and make sure that they
can be shared with a certain degree ofprivacy guarantee while at the same
time preserves the utility of the data?
    - Information Exposure Detection: new information retrieval and natural
language processing technologies are needed to quickly identify components
and/or at tributes of a user's online public profile that may reduce the
user's privacy, and warn one's vulnerability on the Web.
    - Novel Information Retrieval
Techniques for Information Privacy/Security Application: new information
retrieval, evaluation, or machine learning techniques need to be designed
that fit the practice of applications in information privacy and security.
    - Private Information Retrieval Techniques for Enabling
Location Privacy in Location-Based Services: data about a user's location
and historical movements can potentially be gathered by a third party who
takes away the information without the awareness of the service providers
and the users, how location-based services and recommender systems interact
with Location Obfuscation techniques and other Privacy-Enhancing
Technologies.

Grace Hui Yang (Georgetown University)
Ian Soboroff (NIST)

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