Video streaming has become increasingly popular in the Internet. Frequently, video transmissions are based on peer‐to‐peer networks, in which peers running on end‐user hosts transmit data among themselves. An important security vulnerability of this strategy is that content can be easily altered by malicious users. Thus, it becomes essential to diagnose and fight content pollution in these systems. In this work, the authors present a novel strategy that relies on comparison‐based diagnosis to mitigate content pollution in live video streaming peer‐to‐peer networks. This strategy is fully distributed and effectively combats the dissemination of content pollution. In the strategy, peers independently identify and avoid polluters. The solution works on top of the scalable overlay network Fireflies. Experimental results are presented showing the effectiveness and the low overhead of the solution. In particular, the strategy was able to significantly reduce content pollution propagation in diverse network configurations.

Comparison-based diagnosis is a practical approach to detect faults in hardware, software, and parallel and distributed systems. Diagnosis is based on the comparison of task outputs returned by pairs of system units. This work introduces a novel diagnosis algorithm to identify faults in$t$-diagnosable systems of arbitrary topology under the MM* model. The complexity of the proposed algorithm is$O(t^2 \Delta N)$in the worst case for a system with$N$units, where$t$denotes the maximum number of faulty units allowed and$\Delta$corresponds to the maximum degree of a unit in the system. This complexity is nearly optimal in the sense that it is very close to that of traversing the syndrome once. Besides the algorithm specification and correctness proofs, simulations results are also presented, showing the typical performance of the algorithm for different systems.