# An Untraceable Data Sharing Scheme in Wireless Sensor Networks
## 4 Smart Adversary Model and Trace-Back Attack  
The adversary plan to hunt the pandas by analyzing the network traffic. To achieve this goal, the adversary employs a set of monitoring devices and we denote them as hunter nodes. Each node consists of an antenna, spectrum analyzer, storage device, processor and mobile device to perform the trace-back attack. As the hunter nodes are well equipped, we assume that they have enough resources to store, process, transmit the monitoring data of the network traffic. A hunter node can intercept the packages transmitted in the monitored area and accurately locate a sender's location by measuring the angle of arrival as well as the strength of the signal. However, we assume that the adversary cannot decrypt the packages to capture the plaintexts. The sensing range of the hunter node is denoted as \(R_{S}\). As the adversary does not need to accurately transfer the signals into digital codes, we can infer that \(R_{S}\) is some larger than \(R_{c}\). To reflect the relation between \(R_{S}\) and \(R_{c}\), we introduce a parameter \(\rho(\rho\geq 1)\) into our scheme and set \(R_{s}=\rho R_{c}\). Note that, though the hunter nodes can locate the senders of the data, they cannot determine the receiver of the data considering that any node in the transmission area of the sender can be the potential receiver. In the initial phase of trace-back attack, the hunter nodes are deployed around the sink node and monitor the surrounding signals. Once a package crosses the monitored area of a hunter node, in theory, the hunter can find that, at most, nearly \(2\rho\) sensor nodes orderly send the package. Then, the hunter node needs to predict the direction of the former nodes that delivered the package and moves forward in the predicted direction by a proper distance \(d\). In this paper, we assume that \(d=R_{s}\). In practice, the pandas spend more time in some areas and less time in other areas. For example, they may tend to live in an area due to the availability of food, water, shadow, shelter, etc. In this case, the hunter nodes can easily find the source node in a collaborative manner if the network employs ordinary routing algorithms, such as the shortest path routing algorithm, Greedy Perimeter Stateless Routing (GPSR) [19], directed diffusion [20], etc. In the worst case, the adversary can even find the pandas by employing only one hunter node as shown in Figure 2a. If the network employs some proper source-location privacy protection schemes such as phantom routing algorithm [8] or anonymous cloud structure [4], the hunter nodes cannot directly locate the source node. However, the adversary  
Figure 1: Network model. can easily sketch the suspicious region that covers some pandas as shown in Figure 2b. In the process of tracing back, the range of the predicted direction increases with the increasing of the distance between the sink node and the hunter node. Then, more hunter nodes are needed to monitor all the possible areas that the packages may cross. In other words, the adversary needs to put more hunters on the routing path as the monitored area moves far from the sink node. Finally, the hunters can always find the border of the suspicious region. Under the assumption of the phantom routing algorithm, the hunters can form a circle around the panda. Specifically, the center of the circle is near to the location of the panda and its radius is about \(k\) times of the average distance between a pair of sensor nodes. In the cloud-based scheme, the hunters can locate the cloud. Once the adversary successfully locates a suspicious region, a good strategy is scattering all the hunter nodes in this region to monitor the signals in fine-grained manner. In the limit case, we can treat the adversary in this region as a global adversary and assume that he knows all the radio actions of the sensor nodes in the suspicious area. Under this assumption, most existing schemes cannot defend against the smart adversary and the panda  
Figure 2: The trace-back attack in different networks.