The open vote network ( [10]) is a secure two-round multi-party protocol facilitating the computation of a sum of integer votes without revealing their individual values. This is done without a central authority trusted for privacy, and thus allows decentralised and anonymous decision-making efficiently. As such, it has also been implemented in other settings such as financial applications, see e.g. [15, 17].An inherent limitation of is its lack of robustness against denial-of-service attacks, which occur when at least one of the voters participates in the first round of the protocol but (maliciously or accidentally) not in the second. Unfortunately, such a situation is likely to occur in any real-world implementation of the protocol with many participants. This could incur serious time delays from either waiting for the failing parties and perhaps having to perform extra protocol rounds with the remaining participants.This paper provides a solution to this problem by extending with mechanisms tolerating a number of unresponsive participants, the basic idea being to run several sub-elections in parallel. The price to pay is a carefully controlled privacy loss, an increase in computation, and a statistical loss in accuracy, which we demonstrate how to measure precisely.

Detection of rare traits or diseases in a large population is challenging. Pool testing allows covering larger swathes of population at a reduced cost, while simplifying logistics. However, testing precision decreases as it becomes unclear which member of a pool made the global test positive.In this paper we discuss testing strategies that provably approach bestpossible strategy -optimal in the sense that no other strategy can give exact results with fewer tests. Our algorithms guarantee that they provide a complete and exact result for every individual, without exceeding 1/0.99 times the number of tests the optimal strategy would require. This threshold is arbitrary: algorithms closer to the optimal bound can be described, however their complexity increases, making them less practical. Moreover, the way the algorithms process input samples leads to some individuals' status to be known sooner, thus allowing to take urgency into account when assigning individuals to tests.