A Privacy-Preserving Approach for Composite Web Service Selection

Web services technologies are considered as the most promising technologies for the integration of applications and heterogeneous data sources. In spite of the progress achieved in this area, the issue of users’ privacy protection remains a major concern for both academic and industrial communities. This paper presents a model for preserving privacy in the context of web services and demonstrates its feasibility in a web service selection scenario. In addition, we introduce three privacy based selection approaches. The first one is based on a best first search like procedure, while the two others are based on two well-known declarative AI models, namely propositional satisfiability (SAT) and answer set programming (ASP). Finally, an experimental evaluation on different types of datasets, demonstrates the effectiveness of our proposed approaches.