DStress: Efficient Differentially Private Computations on Distributed Data

In this paper, we present DStress, a system that can efficiently perform computations on graphs that contain confidential data. DStress assumes that the graph is physically distributed across many participants, and that each participant only knows a small subgraph; it protects privacy by enforcing tight, provable limits on how much each participant can learn about the rest of the graph. We also study one concrete instance of this problem: measuring systemic risk in financial networks. Systemic risk is the likelihood of cascading bankruptcies – as, e.g., during the financial crisis of 2008 – and it can be quantified based on the dependencies between financial institutions; however, the necessary data is highly sensitive and cannot be safely disclosed. We show that DStress can implement two different systemic risk models from the theoretical economics literature. Our experimental evaluation suggests that DStress can run the corresponding computations in about five hours, whereas a na¨ıve approach could take several decades.