Several efforts have been made for more privacy aware Online Social Networks (OSNs) to protect personal data against various privacy threats. However, despite the relevance of these proposals, we believe there is still the lack of a conceptual model on top of which privacy tools have to be designed. Central to this model should be the concept of risk. Therefore, in this paper, we propose a risk measure for OSNs. The aim is to associate a risk level with social network users in order to provide other users with a measure of how much it might be risky, in terms of disclosure of private information, to have interactions with them. We compute risk levels based on similarity and benefit measures, by also taking into account the user risk attitudes. In particular, we adopt an active learning approach for risk estimation, where user risk attitude is learned from few required user interactions. The risk estimation process discussed in this paper has been developed into a Facebook application and tested on real data. The experiments show the effectiveness of our proposal.

Privacy in social networks: How risky is your social graph?

CARMINATI, BARBARA;FERRARI, ELENA
2012-01-01

Abstract

Several efforts have been made for more privacy aware Online Social Networks (OSNs) to protect personal data against various privacy threats. However, despite the relevance of these proposals, we believe there is still the lack of a conceptual model on top of which privacy tools have to be designed. Central to this model should be the concept of risk. Therefore, in this paper, we propose a risk measure for OSNs. The aim is to associate a risk level with social network users in order to provide other users with a measure of how much it might be risky, in terms of disclosure of private information, to have interactions with them. We compute risk levels based on similarity and benefit measures, by also taking into account the user risk attitudes. In particular, we adopt an active learning approach for risk estimation, where user risk attitude is learned from few required user interactions. The risk estimation process discussed in this paper has been developed into a Facebook application and tested on real data. The experiments show the effectiveness of our proposal.
2012
Proceedings of the 28th International Conference on Data Engineering (ICDE)
the 28th International Conference on Data Engineering, ICDE 2012,April 1-5, 2012, Washington, USA. IEEE Computer Society 2012
Washington, USA
1-5 Aprile, 2012
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/1742482
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 48
  • ???jsp.display-item.citation.isi??? 38
social impact