Name
Title | ||
|---|---|---|
A machine-learning based approach to privacy-aware information-sharing in mobile social networks. |
Abstract | ||
|---|---|---|
Contextual information about users is increasingly shared on mobile social networks. Examples of such information include users’ locations, events, activities, and the co-presence of others in proximity. When disclosing personal information, users take into account several factors to balance privacy, utility and convenience — they want to share the “right” amount and type of information at each time, thus revealing a selective sharing behavior depending on the context, with a minimum amount of user interaction. In this article, we present SPISM, a novel information-sharing system that decides (semi-)automatically, based on personal and contextual features, whether to share information with others and at what granularity, whenever it is requested. SPISM makes use of (active) machine-learning techniques, including cost-sensitive multi-class classifiers based on support vector machines. SPISM provides both ease of use and privacy features: It adapts to each user’s behavior and predicts the level of detail for each sharing decision. Based on a personalized survey about information sharing, which involves 70 participants, our results provide insight into the most influential features behind a sharing decision, the reasons users share different types of information and their confidence in such decisions. We show that SPISM outperforms other kinds of policies; it achieves a median proportion of correct sharing decisions of 72% (after only 40 manual decisions). We also show that SPISM can be optimized to gracefully balance utility and privacy, but at the cost of a slight decrease in accuracy. Finally, we assess the potential of a one-size-fits-all version of SPISM. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.pmcj.2015.01.006 | Pervasive and Mobile Computing |
Keywords | Field | DocType |
Information-sharing,Decision-making,Machine learning,User study,Privacy | Contextual information,World Wide Web,Social network,Computer science,Level of detail,Usability,Support vector machine,Computer network,Group information management,Human–computer interaction,Personally identifiable information,Information sharing | Journal |
Volume | Issue | ISSN |
25 | C | 1574-1192 |
Citations | PageRank | References |
14 | 0.61 | 26 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Igor Bilogrevic | 1 | 195 | 13.82 |
| Kévin Huguenin | 2 | 264 | 21.67 |
| Berker Agir | 3 | 63 | 2.57 |
| Murtuza Jadliwala | 4 | 266 | 25.26 |
| Maria Gazaki | 5 | 14 | 0.61 |
| J. -P. Hubaux | 6 | 10006 | 772.23 |