Recent scientific results have shown that social network Likes, such as the “Like Button” records of Facebook, can be used to automatically and accurately predict even highly sensitive personal attributes. Although this could be the goal of a number of non-malicious activities, to improve products, services, and targeting, it represents a dangerous invasion of privacy with possible intolerable consequences. However, completely defusing the information power of Likes appears improper. In this paper, we propose a protocol able to keep Likes unlinkable to the identity of their authors, in such a way that the user may choose every time she expresses a Like, those non-identifying (even sensitive) attributes she wants to reveal. This way, analysis anonymously relating Likes to various characteristics of people is preserved, with no risk for users’ privacy. The protocol is shown to be secure and also ready to the possible future evolution of social networks towards P2P fully distributed models.

Analysis-preserving protection of user privacy against information leakage of social-network Likes

Buccafurri F
;
Lax G;
2016

Abstract

Recent scientific results have shown that social network Likes, such as the “Like Button” records of Facebook, can be used to automatically and accurately predict even highly sensitive personal attributes. Although this could be the goal of a number of non-malicious activities, to improve products, services, and targeting, it represents a dangerous invasion of privacy with possible intolerable consequences. However, completely defusing the information power of Likes appears improper. In this paper, we propose a protocol able to keep Likes unlinkable to the identity of their authors, in such a way that the user may choose every time she expresses a Like, those non-identifying (even sensitive) attributes she wants to reveal. This way, analysis anonymously relating Likes to various characteristics of people is preserved, with no risk for users’ privacy. The protocol is shown to be secure and also ready to the possible future evolution of social networks towards P2P fully distributed models.
File in questo prodotto:
File Dimensione Formato  
Buccafurri_2015_j.ins_Analysis_post.pdf

embargo fino al 06/09/2017

Tipologia: Documento in Post-print
Licenza: Creative commons
Dimensione 307.08 kB
Formato Adobe PDF
307.08 kB Adobe PDF Visualizza/Apri
Buccafurri_2015_j.ins_Analysis_Editor.pdf

non disponibili

Descrizione: File editoriale
Tipologia: Versione Editoriale (PDF)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 728.57 kB
Formato Adobe PDF
728.57 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

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: http://hdl.handle.net/20.500.12318/1458
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 26
  • ???jsp.display-item.citation.isi??? 22
social impact