Privacy-preserving mining of sequential association rules from provenance workflows

Abstract

Provenance workflows capture movement and transformation of data in complex environments, such as document management in large organizations, content generation and sharing in in social media, scientific computations, etc. Sharing and processing of provenance workflows brings numerous benefits, e.g., improving productivity in an organization, understanding social media interaction patterns, etc. However, directly sharing provenance may also disclose sensitive information such as confidential business practices, or private details about participants in a social network. We propose an algorithm that privately extracts sequential association rules from provenance workflow datasets. Finding such rules has numerous practical applications, such as capacity planning or identifying hot-spots in provenance graphs. Our approach provides good accuracy and strong privacy, by leveraging on the exponential mechanism of differential privacy. We propose an heuristic that identifies promising candidate rules and makes judicious use of the privacy budget. Experimental results show that the our approach is fast and accurate, and clearly outperforms the state-of-the-art. We also identify influential factors in improving accuracy, which helps in choosing promising directions for future improvement.