Abstract:

Concerning the scenario that business data is published for privacy protection prior to its lineage workflow, a novel privacy-preserving lineage workflow model (θ,γ)-POMS is devised. The internal correlation mechanism between the lineage workflow and data is constructed by introducing the anonymous domain and applying the decision tree algorithm. Furthermore, an effective privacy-preserving lineage publishing method based on (θ,γ)-POMS model is proposed to achieve the privacy security in the lineage workflow publishing while achieving the privacy security in the data publishing. The concept of module fuzziness was introduced to measure the degree of module privacy disclosure, and the restricted publishing strategy was adopted to destroy the mapping relationship of modules, so as to maintain the stability of the structure of the lineage workflow while protecting the privacy security of modules.T heoretical analysis and experimental results show the proposed methods can maintain the availability of structured queries of the lineage workflow while ensure the privacy security of lineage workflow.

Key words: provenance, privacy-preserving, provenance publication