Title
R-Susceptibility: An IR-Centric Approach to Assessing Privacy Risks for Users in Online Communities.
Abstract
Privacy of Internet users is at stake because they expose personal information in posts created in online communities, in search queries, and other activities. An adversary that monitors a community may identify the users with the most sensitive properties and utilize this knowledge against them (e.g., by adjusting the pricing of goods or targeting ads of sensitive nature). Existing privacy models for structured data are inadequate to capture privacy risks from user posts. This paper presents a ranking-based approach to the assessment of privacy risks emerging from textual contents in online communities, focusing on sensitive topics, such as being depressed. We propose ranking as a means of modeling a rational adversary who targets the most afflicted users. To capture the adversary's background knowledge regarding vocabulary and correlations, we use latent topic models. We cast these considerations into the new model of R-Susceptibility, which can inform and alert users about their potential for being targeted, and devise measures for quantitative risk assessment. Experiments with real-world data show the feasibility of our approach.
Year
DOI
Venue
2016
10.1145/2911451.2911533
SIGIR
Keywords
Field
DocType
Privacy Risk Assessment,Online Communities
Data mining,Internet privacy,Privacy by Design,Ranking,Computer science,Personally identifiable information,Adversary,Topic model,Information privacy,Privacy software,The Internet
Conference
Citations 
PageRank 
References 
9
0.46
24
Authors
6
Name
Order
Citations
PageRank
Joanna Biega122510.91
P. Krishna Gummadi27961511.50
Ida Mele37310.95
Dragan Milchevski4624.26
Christos Tryfonopoulos524621.99
Gerhard Weikum6127102146.01