The Library
Learning to un-rank : quantifying search exposure for users in online communities
Tools
Biega, J. Asia , Ghazimatin, Azin , Ferhatosmanoglu, Hakan , Gummadi , Krishna P. and Weikum, Gerhard (2017) Learning to un-rank : quantifying search exposure for users in online communities. In: CIKM 2017 : The 26th 2017 ACM Conference on Information and Knowledge Management, Singapore, 6-10 Nov 2017. Published in: Proceedings of the 26th International Conference on Information and Knowledge Management (CIKM) 267-276 . ISBN 9781450349185 .
|
PDF
WRAP-learning-un-rank-quantifying-exposure-communities-Ferhatosmanoglu-2017.pdf - Accepted Version - Requires a PDF viewer. Download (1078Kb) | Preview |
Official URL: https://doi.org/10.1145/3132847.3133040
Abstract
Search engines in online communities such as Twitter or Facebook not only return matching posts, but also provide links to the profiles of the authors. Thus, when a user appears in the top-k results for a sensitive keyword query, she becomes widely exposed in a sensitive context. The effects of such exposure can result in a serious privacy violation, ranging from embarrassment all the way to becoming a victim of organizational discrimination.
In this paper, we propose the first model for quantifying search exposure on the service provider side, casting it into a reverse k-nearest-neighbor problem. Moreover, since a single user can be exposed by a large number of queries, we also devise a learning-to-rank method for identifying the most critical queries and thus making the warnings user-friendly. We develop efficient algorithms, and present experiments with a large number of user profiles from Twitter that demonstrate the practical viability and effectiveness of our framework.
Item Type: | Conference Item (Paper) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | |||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
Library of Congress Subject Headings (LCSH): | Search engines -- Programming, Information retrieval, Twitter (Firm) | |||||||||
Journal or Publication Title: | Proceedings of the 26th International Conference on Information and Knowledge Management (CIKM) | |||||||||
Publisher: | ACM | |||||||||
ISBN: | 9781450349185 | |||||||||
Official Date: | 5 August 2017 | |||||||||
Dates: |
|
|||||||||
Page Range: | 267-276 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||
Date of first compliant deposit: | 19 September 2017 | |||||||||
Date of first compliant Open Access: | 28 September 2017 | |||||||||
RIOXX Funder/Project Grant: |
|
|||||||||
Conference Paper Type: | Paper | |||||||||
Title of Event: | CIKM 2017 : The 26th 2017 ACM Conference on Information and Knowledge Management | |||||||||
Type of Event: | Conference | |||||||||
Location of Event: | Singapore | |||||||||
Date(s) of Event: | 6-10 Nov 2017 |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year