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.

Working with sensitive data is often a balancing act between privacy and integrity concerns. Consider, for instance, a medical researcher who has analyzed a patient database to judge the effectiveness of a new treatment and would now like to publish her findings. On the one hand, the patients may be concerned that the researcher's results contain too much information and accidentally leak some private fact about themselves; on the other hand, the readers of the published study may be concerned that the results contain too little information, limiting their ability to detect errors in the calculations or flaws in the methodology.

The problem of flash data dissemination refers to spreading dynamically-created medium-sized data to all members of a large group of users. In this paper, we explore a solution to the problem of flash data dissemination in unstructured P2P networks and propose a gossip-based protocol, termed Catalogue-Gossip. Our protocol alleviates the shortcomings of prior gossip-based dissemination approaches through the introduction of an efficient catalogue exchange scheme that helps reduce unnecessary interactions among nodes in the unstructured network. We provide deterministic guarantees for the termination of the protocol and suggest optimizations concerning the order with which pieces of flash data are assembled at receiving peers. Experimental results show that Catalogue-Gossip is significantly more efficient than existing solutions when it comes to delivery of flash data.