## 5 Evaluation and Discussion
### Security of WSN Data Processing  
#### 5.2.1 Privacy Preservation against Eavesdropping  
This section analyzes the framework against the attacker model, Eavesdropper (\(E\)). The \(E\) is interested in learning information about the state of a building equipped with the presented framework by eavesdropping on wireless transmissions generated by nodes of the WSN. There is evidence [15; 16; 17] that in the typical WSN scenario where wireless traffic is secured by encryption at data-link-layer or Transport Layer Security analogous, an \(E\) could analyze large amounts of WSN traffic to extract descriptive features such as transmission size, occurrence frequency, processing delay, etc. Therefore, the associating of these features with facts or secrets and applying data mining techniques could allow the \(E\) to infer over current activities in the monitored region by observing the network traffic. In the following, we show that the proposed framework withstands traffic analysis attacks sourcing data from the eavesdropping of wireless transmissions of WSN nodes. We motivate the focus on this type of attack by the following three aspects: (a) easy to launch in the indoor monitoring WSN setting; (b) the attack cannot be detected since the attacker is not interfering with the normal functioning of the WSN, and the attack does not require physical access to WSN nodes; and (c) in addition to disclosing privacy, the extracted information could favor other attacks aiming to compromise building security. The proposed decentralized framework specifies wireless communication involving sink-to-sensor or sensor-to-sensor transmissions. Therefore, the component responsible for ensuring these transmissions' privacy is the DQ3P protocol described in Section 4.3. The DQ3P is a querying protocol in which queries are issued from sink nodes in the form of an onion message. The onion messages are forwarded through a sequence of nodes completing its circuit-like path at the origin sink node. In the following, we recall the terminology defined in Section 4.3. DQ3P guarantees data privacy against eavesdropping since onion messages are secured by encryption. DQ3P generates uniform network traffic that does not leak contextual information about activities in the monitored environment due to the following properties:  
1. Sensor nodes are not reporting data to the sink node, but the sink node queries sensor nodes. 2. All onion messages traveling the network are of the same size since padding is added at each message processing. 3. DQ3P requires encryption at data-link-layer; therefore, after each message processing, the onion message is forwarded to the next-hop node completely changed by encryption. 4. By DQ3P design, half of the nodes processing an onion message only retain it for a time interval to simulate model learning. We refer to these nodes as decoy nodes. 5. Onion messages travel a randomized path since decoy node addresses are encoded at random positions in the message path during message construction. Therefore, by property 1, onion message transmission does not imply activity in the monitored environment. By properties, 2, 3, 4, and 5 nodes contributing to model learning cannot be identified by eavesdropping on wireless transmissions. Moreover, if multiple onion messages are issued to the WSN, these properties allow onion messages to mix as they travel through the network. Therefore, even if considering \(E\) as able to intercept the whole wireless traffic of the WSN. Due to the mixing of onion messages, it becomes challenging for \(E\) even to track the circuit-like onion message path. Evaluation of DQ3P for Distributed Data Mining  
This section presents simulation results of DQ3P, the communication protocol for secure data processing detailed in Section 4.3. The investigation aims to assess the delays introduced by DQ3P applied to train the data mining model described in Section 4.2 on nodes of a WSN. To conduct the investigation, we base it on the results from the previous study [37].