IoT personal devices are having an undoubtedly explosive growth as many different products become available on the market such as smart watches, contact lenses, fitness bands, microchips under skin, among others. These devices and their related applications process the collected sensor data and use them to provide services to their users; in addition most of them require data from other applications in order to enhance their service. However, data sharing increases the risk for privacy protection since the aggregation of multiple data may favor the prediction of unrevealed private information. This paper presents a general framework for managing the issue of privacy protection from unwanted disclosure of personal data. The framework integrates two approaches in privacy protection: the use of personal data managers to control and manage user data sharing and the use of techniques for inference prevention. The contribution of the framework is to exploit the advantages of the two mentioned lines of research in a user-centric approach that empowers users with higher control of their data and makes them aware about data-sharing decisions.

A Framework for Personal Data Protection in the IoT

TORRE, ILARIA;KOCEVA, FROSINA;SANCHEZ, ODNAN REF;ADORNI, GIOVANNI
2016-01-01

Abstract

IoT personal devices are having an undoubtedly explosive growth as many different products become available on the market such as smart watches, contact lenses, fitness bands, microchips under skin, among others. These devices and their related applications process the collected sensor data and use them to provide services to their users; in addition most of them require data from other applications in order to enhance their service. However, data sharing increases the risk for privacy protection since the aggregation of multiple data may favor the prediction of unrevealed private information. This paper presents a general framework for managing the issue of privacy protection from unwanted disclosure of personal data. The framework integrates two approaches in privacy protection: the use of personal data managers to control and manage user data sharing and the use of techniques for inference prevention. The contribution of the framework is to exploit the advantages of the two mentioned lines of research in a user-centric approach that empowers users with higher control of their data and makes them aware about data-sharing decisions.
File in questo prodotto:
File Dimensione Formato  
2016-Torre-Framework-IoT-Privacy.pdf

accesso chiuso

Descrizione: Research paper
Tipologia: Documento in versione editoriale
Dimensione 645.05 kB
Formato Adobe PDF
645.05 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: https://hdl.handle.net/11567/859237
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 31
  • ???jsp.display-item.citation.isi??? 16
social impact