Privacy-preserving publication of provenance workflows

Provenance workflows capture the data movement and the operations changing the data in complex applications such as scientific computations, document management in large organizations, content generation in social media, etc. Provenance is essential to understand the processes and operations that data undergo, and many research efforts focused on modeling, capturing and analyzing provenance information. Sharing provenance brings numerous benefits, but may also disclose sensitive information, such as secret processes of synthesizing chemical substances, confidential business practices and private details about social media participants' lives. In this paper, we study privacy-preserving provenance workflow publication using differential privacy. We adapt techniques designed for sanitization of multi-dimensional spatial data to the problem of provenance workflows. Experimental results show that such an approach is feasible to protect provenance workflows, while at the same time retaining a significant amount of utility for queries. In addition, we identify influential factors and trade-offs that emerge when sanitizing provenance workflows.