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://ift.tt/1Q1Ydlh 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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