E-notebook Middleware for Accountability and Reputation Based Trust in Distributed Data Sharing Communities

This paper presents the design of a new middleware which provides support for trust and accountability in distributed data sharing communities. One application is in the context of scientific collaborations. Multiple researchers share individually collected data, who in turn create new data sets by performing transformations on existing shared data sets. In data sharing communities building trust for the data obtained from others is crucial. However, the field of data provenance does not consider malicious or untrustworthy users. By adding accountability to the provenance of each data set, this middlware ensures data integrity insofar as any errors can be identified and corrected. The user is further protected from faulty data by a trust view created from past experiences and second-hand recommendations. A trust view is based on real world social interactions and reflects each user’s own experiences within the community. By identifying the providers of faulty data and removing them from a trust view, the integrity of all data is enhanced