Abstract
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.
Highlights
The work [19] found out that Lu et al.’s protocol in [7] still has some secure flaws such as user anonymity and mutual authentication, and it presents an improved mobile-healthcare emergency system based on extended chaotic maps for applications of wireless body sensor networks (BSNs)
Li’s Scheme, which is based on oblivious transfer protocol and involves no TP, has a primary disadvantage that one participant may acquire all results of distance computation and deduce some private information of the other participant
During the running of the TPDEP protocol, all messages received by Node A can be denoted by msgT = {s, p, Q1}, which includes the following: Q1 = QT + Rb, p = (Ra − Rb) ∗ (Ra − Rb)T − q
Summary
With the development of the computer and communication technologies, wireless sensor networks have gradually extended into the fabric of human society. Communications between people and things have become a reality so that many researchers have begun to pay attention to wireless sensor networks, where many techniques [1, 2] require secure authentication and secure privacy computation. Location information becomes important and confidential in the above scenarios, though the utilization of the respective positions of two participants is the most common method for calculating Euclidean distance. In this case, secure two-party distance computation without revealing location information becomes a vital problem.
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