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This work focuses on: (1) understanding the impact of selective forwarding attacks on tree-based routing topologies in Wireless Sensor Networks (WSNs), and (2) investigating cryptography-based strategies to limit network degradation caused by sinkhole attacks. The main motivation of our research stems from the following observations. First, WSN protocols that construct a fixed routing topology may be significantly affected by malicious attacks.

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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. This paper presents VerDP, a system for private data analysis that provides both strong integrity and strong differential privacy guarantees. VerDP accepts queries that are written in a special query language, and it processes them only if a) it can certify them as differentially private, and if b) it can prove the integrity of the result in zero knowledge. Our experimental evaluation shows that VerDP can successfully process several different queries from the differential privacy literature, and that the cost of generating and verifying the proofs is practical: for example, a histogram query over a 63,488-entry data set resulted in a 20 kB proof that took 32 EC2 instances less than two hours to generate, and that could be verified on a single machine in about one second.

In this paper, we discuss the problem of detecting general faults in distributed systems that handle confidential information. Detecting non-crash faults is difficult in this setting because, to check the behavior of a given node, we need to know its expected behavior -but that can depend on the confidential information. Classical zero-knowledge proofs are difficult to apply because they are designed to verify functions with a fixed number of inputs, but in many distributed systems, both the size and the number of a node's "inputs" (the messages it has received from other nodes) are not known.We propose an approach that can efficiently provide zeroknowledge fault detection for certain systems. Our approach spreads the detection tasks across multiple nodes, leveraging a node's existing knowledge whenever possible. We use epistemic reasoning to infer such knowledge, and we combine classical zero-knowledge proofs with a special data structure to handle inputs of unknown size. We show how our approach can be applied to a simple example system, and we report some initial performance measurements.