Name
Abstract | ||
|---|---|---|
This paper proposes a new model of user-centric, global, probabilistic privacy, geared for today's challenges of helping users to manage their privacy-sensitive information across a wide variety of social networks, online communities, QA forums, and search histories. Our approach anticipates an adversary that harnesses global background knowledge and rich statistics in order to make educated guesses, that is, probabilistic inferences at sensitive data. We aim for a tool that simulates such a powerful adversary, predicts privacy risks, and guides the user. In this paper, our framework is specialized for the case of Internet search histories. We present preliminary experiments that demonstrate how estimators of global correlations among sensitive and non-sensitive key-value items can be fed into a probabilistic graphical model in order to compute meaningful measures of privacy risk. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2663715.2669609 | PSBD@CIKM |
Keywords | Field | DocType |
probabilistic privacy,privacy risk prediction,user-centric privacy,privacy,data mining,query logs | Data mining,Social network,Information retrieval,Computer science,Probabilistic logic,Adversary,Graphical model,Privacy software,The Internet,Estimator | Conference |
Citations | PageRank | References |
6 | 0.52 | 27 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joanna Biega | 1 | 225 | 10.91 |
| Ida Mele | 2 | 6 | 0.52 |
| Gerhard Weikum | 3 | 12710 | 2146.01 |