Analyzing Privacy Policies Based on a Privacy-Aware Profile: The Facebook and LinkedIn Case Studies

Abstract

The regular use of social networking websites and applications encompasses the collection and retention of personal and very often sensitive information about users. This information needs to remain private and each social network owns a privacy policy that describes in-depth how users' information is managed and disclosed. Problems arise when the development of new systems and applications includes an integration with social networks. The lack of clear understanding and a precise mechanism to enforce the statements described in privacy policies can compromise the development and adaptation of these statements. This paper proposes the extension and validation of a UML profile for privacy-aware systems. The goal of this approach is to provide a better understanding of the different privacy-related requirements for improving privacy policies enforcement when developing systems or applications integrated with social networks. Additionally, to illustrate the potential of this profile, the paper presents and discusses its application with two real world case studies - the Facebook and Linked In policies - which are well structured and represented through two respective Excel files.