Name
Abstract | ||
|---|---|---|
From buying books to finding the perfect partner, we share our most intimate wants and needs with our favourite online systems. But how far should we accept promises of privacy in the face of personalized profiling? In particular, we ask how we can improve detection of sensitive topic profiling by online systems. We propose a definition of privacy disclosure that we call ϵ-indistinguishability, from which we construct scalable, practical tools to assess the learning potential from personalized content. We demonstrate our results using openly available resources, detecting a learning rate in excess of 98% for a range of sensitive topics during our experiments. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2937754 | ACM Trans. Priv. Secur. |
Keywords | DocType | Volume |
Privacy,detection,distinguishability,profiling,search,recommender-system,Bayesian-inference | Journal | abs/1504.08043 |
Issue | ISSN | Citations |
1 | 2471-2566 | 1 |
PageRank | References | Authors |
0.35 | 17 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pól Mac Aonghusa | 1 | 25 | 7.93 |
| Douglas J. Leith | 2 | 1332 | 116.75 |