Enriching the Open Provenance Model for a Privacy-Aware Provenance Management

Abstract

Today, the total amount of data that is generated, copied, and stored are increasing rapidly. Thereupon, the trustworthiness of the data source and the quality of data have significant importance for an effective data analysis. Therefore, it is critical to improve accountability for the quality of data. For this purpose, provenance information is used to provide the quality of data. Provenance information ensures the reliability and quality of data. Data provenance is a form of metadata to describe the life cycle of a data. Therefore, provenance information maintains the history of the data by describing how data are derived. The Open Provenance Model (OPM) aims to meet the requirements of a provenance model. For this purpose, OPM defines a core set of rules. Thus, OPM provides provenance interoperability. In this study, OPM is enhanced to provide a Privacy-Aware Provenance Management (PAPM) model. The goal of the PAPM model is to use provenance information in order to protect data from unwanted access and detect security violations. Therefore, PAPM uses provenance information to protect data privacy. Since the proposed PAPM model is domain-independent, it can be integrated into any interested domain to preserve privacy and ensure data security.