Compute globally, act locally: protecting federated systems from systemic threats

Building dependable federated systems is often complicated by privacy concerns: if the domains are not willing to share information with each other, a global or 'systemic' threat may not be detected until it is too late. In this paper, we study this problem using a somewhat unusual example: the financial crisis of 2008. Based on results from the economics literature, we argue that a) the impending crisis could have been predicted by performing a specific distributed computation on the financial information of each bank, but that b) existing tools, such as secure multiparty computation, do not offer enough privacy to make participation safe from the banks' perspective. We then sketch the design of a system that can perform this (and possibly other) computation at scale with strong privacy guarantees. Results from an early prototype suggest that the computation and communication costs are reasonable.