## 6 Experiments
### Selecting Background Knowledge Configurations with Low-Entropy Feature Frequency
#### 6.5.1 Wi-Fi
Exclusively considering location with low-entropy frequency would not represent the correct risk value. Furthermore, as the percentage of low-entropy and location \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Attack Type** & **Feature** & **k** & & \multicolumn{2}{c}{**\%**} & \\  & & & **10** & **20** & **30** & **40** & **50** \\ \hline
**Location Sequence** & Location & 1 & 3.61 \(\times\) 10\({}^{-92}\) & 1.87 \(\times\) 10\({}^{-54}\) & 1.27 \(\times\) 10\({}^{-28}\) & 3.89 \(\times\) 10\({}^{-15}\) & 8.05 \(\times\) 10\({}^{-8}\) \\
**Visit** & Location & 1 & 0.0030071 & 0.1253917 & 0.6209664 & 0.8893009 & 0.9930435 \\
**Location Sequence** & Location & 2 & 0.0003552 & 0.4015571 & 0.9977175 & 0.9999999 & 1 \\
**Visit** & Location & 2 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 \\
**Visit** & Time & 1 & 0.0156405 & 0.2993241 & 0.8144727 & 0.9833140 & 0.9992201 \\
**Visit** & Time & 2 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Wi-Fi dataset: \(p\)-values for different attacks using location and time, comparing approaches. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Attack Type** & **Feature** & **k** & & \multicolumn{2}{c}{**\%**} & \\  & & & **10** & **20** & **30** & **40** & **50** \\ \hline
**Location Sequence** & Location & 1 & 0.0867411 & 0.0665860 & 0.0481105 & 0.0347508 & 0.0246375 \\
**Visit** & Location & 1 & 0.0152179 & 0.0099290 & 0.0063554 & 0.0048902 & 0.0036037 \\
**Location Sequence** & Location & 2 & 0.0175428 & 0.0075378 & 0.0033200 & 0.0015002 & 0.0015002 \\
**Visit** & Location & 2 & 5.85 \(\times\) 10\({}^{-6}\) & 5.85 \(\times\) 10\({}^{-6}\) & 5.85 \(\times\) 10\({}^{-6}\) & 5.85 \(\times\) 10\({}^{-6}\) & 5.85 \(\times\) 10\({}^{-6}\) \\
**Visit** & Time & 1 & 0.0131452 & 0.0082137 & 0.0053548 & 0.0038896 & 0.0031034 \\
**Visit** & Time & 2 & 5.85 \(\times\) 10\({}^{-6}\) & 5.85 \(\times\) 10\({}^{-6}\) & 5.85 \(\times\) 10\({}^{-6}\) & 5.85 \(\times\) 10\({}^{-6}\) & 5.85 \(\times\) 10\({}^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Wi-Fi dataset: Ks-statistics for different attacks using location and time, comparing approaches. frequency increases from 10% to 50%, the \(p\)-values also increase, suggesting a higher degree of similarity between risk assessments. This reinforces the idea that we cannot accurately represent the risk using only a few values and low-entropy location frequency. Table 1's fourth line presents \(p\)-values for various attacks using location to compare approaches, considering a knowledge size \(k\) of 2 and a visit attack. The \(p\)-values remain consistent across different percentages of low-entropy features (10% to 50%). For all percentages, the \(p\)-values are consistently equal to 1 in the visit attack with knowledge size 2, indicating the same results regardless of the percentage of low-entropy features considered. The same can be observed in statistics results in Table 2; all values are equal to zero, meaning that the distributions are very similar. As the number of sets containing unique information, leading to maximum risk, increases, it becomes more probable that one of the selected sets based on entropy and location frequency will contain a location with the lowest entropy value. Consequently, as the percentage of selected locations rises, so does the likelihood of selecting a set with maximum risk. This underscores the importance of considering the knowledge size and feature uniqueness when conducting risk assessment computation improvement using entropy and frequency. Table 1, in the fifth line, presents \(p\)-values for a visit attack using time, comparing approaches. The \(p\)-values and knowledge size are calculated for various scenarios (denoted by different percentages). When the \(k\) value equals 1, the \(p\)-values are very low (close to zero) across different percentages, demonstrating a significant difference in distributions. Similar to the location experiments, the time shows a similar trend.