Mashups are novel content created by extracting and combining data and services from diverse data sources, in an automated manner, using Web services. The Web 2.0 technologies make it easier for individuals to create contents in third party service sites or clouds, and make easier for other third party mashup organizations to access and combine individuals' data and content through mashups. A big unaddressed challenge is how to adequately protect the privacy of individuals when information about them in the data sources are to be accessed and joined by mashup providers, which is different from two-party interaction (i.e. an individual and Web sites) as in many online environment. . In this paper, we present a Privacy Specification and Enforcement Model for Mashups that considers privacy preferences expressed in three different logical networks: personal privacy policies (PPP), data source organization's privacy policies (SPP) and mashup organization's privacy policies (MPP). We present a privacy policy model using a multi-dimensional privacy protection space which includes parameters to specify private content, provider type, mashup-specific operators, and mashup purposes, using 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. The prototype architecture for the proposed mashup privacy protection engine is presented that interacts with distributed privacy policy networks to determine privacy-preserving data sharing and integration for mashup services.
REF presented a framework of privacy-preserving data sharing and integration for mashup service.
Privacy policy-driven mashups
International Journal of Business Continuity and Risk Management
Economics