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 …

We develop a new privacy-preserving framework for a general class of financial network
models, leveraging cryptographic principles from secure multiparty computation and …

Sensitivity and computational complexity in financial networks - IOS Press You are viewing a
javascript disabled version of the site. Please enable Javascript for this site to function properly …

Recent growth in the size and scope of databases has resulted in more research into
making productive use of this data. Unfortunately, a significant stumbling block which …

In several domains, privacy presents a significant obstacle to scientific and analytic
research, and limits the economic, social, health and scholastic benefits that could be …

Secure multi-party computation (MPC) allows multiple parties to perform a joint computation
without disclosing their private inputs. Many real-world joint computation use cases …

Secure multi-party computation (MPC) is a cryptographic primitive for computing on private
data. MPC provides strong privacy guarantees, but practical adoption requires high-quality …

This work studies the problem of MPC decentralization-that is, identifying a set of computing
nodes to securely and efficiently execute the multi-party computation protocol (MPC) over a …

Determining the causes of instability and contagion in financial networks is necessary to
inform policy and avoid future financial collapse. In the American Economic Review, Elliott …

The primary focus of our work has been on develo** novel techniques and algorithms that
add accountability to distributed systems. Accountability adds a new dimension to the …