Generalization Based Privacy-Preserving Provenance Publishing

Abstract

With thriving of data sharing, demands of data provenance publishing become increasingly urgent. Data provenance describes about how data is generated and evolves with time. Data provenance has many applications, in-cluding evaluation of data quality, audit trail, replication recipes, data citation, etc. Some in-out mapping relations and related intermediate parameters in data provenance may be private. How to protect the privacy in the data provenance publishing attracts increasing attention from researchers in recent years. Existing solutions rely primarily on Γ-privacy model, hiding certain properties to solve the module’s privacy-preserving problem. However, the Γ-privacy model has the following disadvantages: (1) The attribute domains are limited. (2) It’s difficult to set consistent Γ value for the workflow. (3) The attribute selection strategy is unreasonable. Concerning these problems, a novel privacy-preserving provenance model is devised to balance the tradeoff between privacy-preserving and utility of data provenance. The devised model applies the generalization and introduces the generalized level. Furthermore, an effective privacy-preserving provenance publishing method based on generalization is proposed to achieve the privacy security in the data provenance publishing. Finally, theoretical analysis and experimental results testifies the effectiveness of our solution.