Sensor networks promise viable solutions to many monitoring problems. However, the practical deployment of sensor networks faces many challenges imposed by real-world demands. Sensor nodes often have limited computation and communication resources and battery power. Moreover, in many applications sensors are deployed in open environments, and hence are vulnerable to physical attacks, potentially compromising the sensor's cryptographic keys.One of the basic and indispensable functionalities of sensor networks is the ability to answer queries over the data acquired by the sensors. The resource constraints and security issues make designing mechanisms for information aggregation in large sensor networks particularly challenging.In this paper, we propose a novel framework for secure information aggregation in large sensor networks. In our framework certain nodes in the sensor network, called aggregators, help aggregating information requested by a query, which substantially reduces the communication overhead. By constructing efficient random sampling mechanisms and interactive proofs, we enable the user to verify that the answer given by the aggregator is a good approximation of the true value even when the aggregator and a fraction of the sensor nodes are corrupted. In particular, we present efficient protocols for secure computation of the median and the average of the measurements, for the estimation of the network size, and for finding the minimum and maximum sensor reading. Our protocols require only sublinear communication between the aggregator and the user. To the best of our knowledge, this paper is the first on secure information aggregation in sensor networks that can handle a malicious aggregator and sensor nodes. *

Abstract. Since the introduction of secure multi-party computation, all proposed protocols that provide security against cheating players suffer from very high communication complexities. The most efficient unconditionally secure protocols among n players, tolerating cheating by up to t < n/3 of them, require communicating O(n 6 ) field elements for each multiplication of two elements, even if only one player cheats. In this paper, we propose a perfectly secure multi-party protocol which requires communicating O(n 3 ) field elements per multiplication. In this protocol, the number of invocations of the broadcast primitive is independent of the size of the circuit to be computed. The proposed techniques are generic and apply to other protocols for robust distributed computations. Furthermore, we show that a sub-protocol proposed in [GRR98] for improving the efficiency of unconditionally secure multi-party computation is insecure.

Abstract. A (k; n)-robust combiner for a primitive F takes as input n candidate implementations of F and constructs an implementation of F, which is secure assuming that at least k of the input candidates are secure. Such constructions provide robustness against insecure implementations and wrong assumptions underlying the candidate schemes. In a recent work Harnik et al. (Eurocrypt 2005) have proposed a (2; 3)-robust combiner for oblivious transfer (OT), and have shown that (1; 2)-robust OT-combiners of a certain type are impossible.In this paper we propose new, generalized notions of combiners for two-party primitives, which capture the fact that in many two-party protocols the security of one of the parties is unconditional, or is based on an assumption independent of the assumption underlying the security of the other party. This fine-grained approach results in OT-combiners strictly stronger than the constructions known before. In particular, we propose an OT-combiner which guarantees secure OT even when only one candidate is secure for both parties, and every remaining candidate is flawed for one of the parties. Furthermore, we present an efficient uniform OT-combiner, i.e., a single combiner which is secure simultaneously for a wide range of candidates' failures. Finally, our definition allows for a very simple impossibility result, which shows that the proposed OT-combiners achieve optimal robustness.

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