Title: Privacy policy-driven mashups

Authors: Soon Ae Chun; Janice Warner; Angelos D. Keromytis

Addresses: School of Business, City University of New York, College of Staten Island, 2800 Victory Boulevard, Staten Island, NY 10314, USA ' School of Business, Georgian Court University, 900 Lakewood Avenue, Lakewood, NJ 08701-2697, USA ' Computer Science Department, Columbia University, 1214 Amsterdam Avenue, New York, NY 10027, USA

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.

Keywords: mashup services; privacy preferences; personal privacy policy; PPP; mashup privacy protection system; distributed privacy policy; privacy policy specifications; privacy policy enforcement; privacy policy discovery and evaluation engine; mashups; semantic resources; information security.

DOI: 10.1504/IJBCRM.2013.058989

International Journal of Business Continuity and Risk Management, 2013 Vol.4 No.4, pp.344 - 370

Received: 07 May 2013
Accepted: 02 Oct 2013

Published online: 31 Mar 2014 *

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