Name
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.
|
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3132847.3133040 | CIKM |
Keywords | Field | DocType |
Search Exposure, Ranking Exposure, Privacy, Information Retrieval | Data mining,World Wide Web,Search engine,Information retrieval,Computer science,Social search,Service provider,Ranging,Embarrassment | Conference |
ISBN | Citations | PageRank |
978-1-4503-4918-5 | 1 | 0.34 |
References | Authors | |
13 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joanna Biega | 1 | 225 | 10.91 |
| Azin Ghazimatin | 2 | 6 | 2.12 |
| Hakan Ferhatosmanoglu | 3 | 1352 | 89.79 |
| P. Krishna Gummadi | 4 | 7961 | 511.50 |
| Gerhard Weikum | 5 | 12710 | 2146.01 |