Numerous privacy-preserving issues have emerged along with the fast development of the Internet of Things. In addressing privacy protection problems in Wireless Sensor Networks (WSN), secure multi-party computation is considered vital, where obtaining the Euclidian distance between two nodes with no disclosure of either side's secrets has become the focus of location-privacy-related applications. This paper proposes a novel Privacy-Preserving Scalar Product Protocol (PPSPP) for wireless sensor networks. Based on PPSPP, we then propose a Homomorphic-Encryption-based Euclidean Distance Protocol (HEEDP) without third parties. This protocol can achieve secure distance computation between two sensor nodes. Correctness proofs of PPSPP and HEEDP are provided, followed by security validation and analysis. Performance evaluations via comparisons among similar protocols demonstrate that HEEDP is superior; it is most efficient in terms of both communication and computation on a wide range of data types, especially in wireless sensor networks.
Scenarios in which two nodes who distrust each other in wireless sensor networks (WSNs) would like to know the distance between them are considered. The scenario is designed to protect the private information of WSNs, in this case each node's location, from the other nodes and from a passive attacker. The goal of the present work is to provide two novel and secure two-party distance computation protocols based on a semihonest model, the first with aid of a third party and the second based on randomization technique. Both of these protocols can extend the calculated value into a real number field. The output of the distance computation and the intermediate values in the proposed protocols are also private and not accessible to a third party or any other attackers. When executing these two protocols, security is guaranteed, and the performances of communication and computation of them are found to be satisfactory when compared to those of other similar protocols.
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