A Framework for Building Privacy-Conscious DaaS Service Mashups

摘要

Data Mashup is a special class of mashup application that combines information on the fly from multiple data sources to respond to transient business needs. Data mashup is a difficult task that would require an important programming skill on the side of mashups' creators, and involves handling many challenging privacy and security concerns raised by data providers. This situation prevents non-expert users from mashing up data at large. In this paper, we present a declarative approach for mashing-up data. The approach allows data mashup creators to build data mashups without any programming involved. The approach builds the mashups automatically and takes into account the data's privacy concerns. We evaluate the efficiency of the approach via a thorough set of experiments. The results show that handling data privacy introduces only a negligible cost in the mashup building time.