Name
Abstract | ||
|---|---|---|
Ensuring privacy of users of social networks is probably an unsolvable conundrum. At the same time, an informed use of the existing privacy options by the social network participants may alleviate - or even prevent - some of the more drastic privacy-averse incidents. Unfortunately, recent surveys show that an average user is either not aware of these options or does not use them, probably due to their perceived complexity. It is therefore reasonable to believe that tools assisting users with two tasks: 1) understanding their social net behavior in terms of their privacy settings and broad privacy categories, and 2)recommending reasonable privacy options, will be a valuable tool for everyday privacy practice in a social network context. This paper presents YourPrivacyProtector, a recommender system that shows how simple machine learning techniques may provide useful assistance in these two tasks to Facebook users. We support our claim with empirical results of application of YourPrivacyProtector to two groups of Facebook users. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.5121/ijsptm.2013.2402 | CoRR |
Field | DocType | Volume |
Recommender system,Internet privacy,Social network,Privacy by Design,Simple machine,Computer security,Computer science,Information privacy,Privacy software | Journal | abs/1602.01937 |
ISSN | Citations | PageRank |
International journal of security, privacy and trust management.
(IJSPTM) Volume 2, No 4, Aug. 2013 | 5 | 0.49 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kambiz Ghazinour | 1 | 89 | 11.91 |
| Stan Matwin | 2 | 3025 | 344.20 |
| Marina Sokolova | 3 | 720 | 28.40 |