Privacy policy-driven mashups

Abstract

Mashups is novel content created by extracting and combining data from diverse data sources. In this paper, we present a framework of privacy-preserving data sharing and integration for mashup services. The mashup privacy protection system evaluates privacy preferences expressed in a distributed privacy policy network, i.e., personal privacy policies (PPP), data source organisation’s privacy policies (SPP) and mashup organisation’s privacy policies (MPP). The privacy policy is expressed in terms of sensitive data to be protected, mashup provider, mashup-operations, and purposes. These parameters can link to the open semantic resources on the web, such as a friend of a friend (FOAF), service industry classification codes and UN product and service classification codes, providing richer semantic reference and avoiding data ambiguity. The mashup privacy protection system allows the specification of privacy policies and enforces distributed privacy policies in creating a new content by a third party mashup provider.