# An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning
## 5 Evaluation
### Experiment Results  
The results of the FVR are shown in Figure 7a. At the beginning, as the supervised learning is not performing, the FVR can achieve its maximum value, which is 25. As time goes by, the FVR gradually declines and eventually levels out at around 13. Figure 7b shows the results of ACA. As time goes by, the ACA decreases gradually, from 0.8 to 0.4, and then waves around 0.3. In the beginning, with the default parameters, the learning algorithm results in very poor privacy protection. Seven days later, the ACA arrives at a stably low level, indicating that good privacy protection has been achieved. As time goes by, the ACA gets lower, along with a continuous decrease in FVR thanks to the execution of the learning algorithm from the sink node and the sending algorithm from the sensor node.  
Figure 8 shows that, with an increase in the FVR, the latencies of the ConstRate and the FitProbRate models gradually decrease. Since, in our method, real data is sent out without any delay, the latency is caused by the multi-hop forwarding time, which can be ignored, in contrast to the other two models. Figure 8 also provides the latency of a sensor node during a period of one day, which shows that the period from (0,6) to (9,17) has a lower latency due to a sparse transmission rate, and the period from (7,9) to (17,24) has a higher latency due to a dense transmission rate. This result indicates that the delay in the FitProbRate model is affected by the density of transmission, and as the transmission rate of the real event goes up, the delay will increase.  
Figure 8: Comparison of latency; (**a**) Description of the change of the Latency which with the change of the FVR; (**b**) Description of the change of Latency of FitProbRate Model which with the change of the hours in a day.  
Figure 7: Relationship between FVR, ACA, and time in the SDASL model; (**a**) Description of the change of the FVR; (**b**) Description of the change of ACA.  
Figure 9 shows that, with an increase in FVR, the ACA gradually decreases. While in the ConstRate model, the ACA remains at a low level, it is affected by FVR in our model and in the FitProbRate model. When FVR increases, ACA declines. As in the FitProbRate model, if real events happen frequently, the overall transmission will become dense. Whereas, if real events happen sparsely, the sequence will become synchronized. Therefore, the adversary can infer the routine of the residents by analyzing the density of the transmission sequence. Accordingly, when the sequence is dense, people may be at home and pursuing activities. When the sequence is sparse, people may be asleep or at work. In our model and in the ConstRate model, this problem does not occur.  
Figure 10 shows that with the increase in ACA, the FVR in all three models gradually decreases. Without considering the delay, the ConstRate model is the most energy-efficient model out of the three models. In the smart home, however, the delay must be controlled within an appropriate range. Compared to the FitProbRate and the SDASL models, for each ACA, the FVR in SDASL is much lower than that in FitProbRate, meaning that, under the same level of privacy protection, FitProbRate will incur 50% more noise data than SDASL. Since energy consumption mainly occurs during data transmission, SDASL will result in a 40% greater energy saving than FitProbRate, when the ACA is 0.2.  
Figure 10: Comparison of energy consumption.  
Figure 9: Comparison of ACA.  
In summary, among the three models, ConstRate can provide the best privacy protection with an average latency and the longest delay. FitProbRate can reduce the delay, but a resident's routine may be revealed and the computation load in the sensor node would increase. Our SDASL model can overcome the shortcomings of the FitProbRate model, while reducing the latency.