Name
Abstract | ||
|---|---|---|
Ensuring privacy of users of social networks is probably an unsolvable conundrum. It seems, however, that 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 network 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 early research that shows how simple machine learning techniques may provide useful assistance in these two tasks to Facebook users. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2457317.2457344 | EDBT/ICDT Workshops |
Keywords | Field | DocType |
social network,everyday privacy practice,social network behavior,existing privacy option,social network participant,ensuring privacy,social network context,broad privacy category,reasonable privacy option,privacy setting,classification,privacy,recommender system,design | Recommender system,Internet privacy,Social network,Simple machine,Privacy by Design,Computer security,Computer science,Information privacy,Privacy software | Conference |
Citations | PageRank | References |
21 | 0.94 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kambiz Ghazinour | 1 | 89 | 11.91 |
| Stan Matwin | 2 | 3025 | 344.20 |
| Marina Sokolova | 3 | 720 | 28.40 |