# Effective Techniques for Protecting the Privacy of Web Users
## 5 Experimental Results  
In this study, we are interested in evaluating the effectiveness of the privacy protection tools PPTs by measuring the effect on the performance the users perceived and how fast the time it takes to load a web page by blocking third-party requests. Therefore, we extracted JavaScript programs, one of the most important web elements that are typically used for tracking services. Figure 4 illustrates the total number of JavaScript programs extracted from the top 50 Alexa websites. In the absence of ad-blocking extensions, we obtained 1578 JavaScript programs. Furthermore, the result presents the total number of allowed JavaScript programs for NoScript, Privacy Badger, uBlock, AdBlock Plus, Ghostery, and Disconnect extensions when they are enabled and configured. For in-page JavaScript programs, we obtained 828 JSec and we found that disconnect blocked more than 40% of the embedded script tags that are necessary for providing high availability and high performance of web pages, which makes them ineligible for being classified as trackers. Followed by Privacy Badger, Adblock Plus, and uBlock with 36%, 24%, and 17% of blocking the essential scripts respectively. As well, Ghostery and NoScript have the lowest percentage of blocking functioning scripts, each with 11%. For more details, (see Table 3). Figure 3: Measurement framework for the privacy protection tools. For external JavaScript programs, we obtained 750 JSec and we found that NoScript is the highest blocker of the external third-party services with 61%. The next two observations involve Privacy Badger and Ghostery with 48% blocking, followed by Disconnect and uBlock with 47% and 44% blocking respectively. And finally, AdBlock Plus provides the least amount of blocking third-party services with only 42%. For more details, (see Table 4). Further analysis is performed to assess the accuracy of the privacy protection tools by measuring the Sensitivity and Specificity based on our labeled dataset of 50 websites. For this, we measure the true positive (TP), false positive (FP), true negative (TN), and false negative (FN) rates of each of the PPTs as shown in Table 5.  
Sensitivity is defined as the probability of correctly identifying the existence of a particular feature or condition. This is also known as the true positive rate (TPR). Consider our case of allow and block functional, Sensitivity relates to the ability of PPTs to efficiently allow functional JavaScript programs. In mathematic terms, this can be expressed as:  
\[\text{Sensitivity (TPR)}=\frac{\text{TP}}{\text{TP}+\text{FN}}\] \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**JSec** & **PPTs Off** & **DC** & **GT** & **ABP** & **UB** & **PB** & **NS** \\ \hline External & 750 & 385 & 372 & 421 & 403 & 373 & 278 \\ \hline Blocked & - & 47\% & 48\% & 42\% & 44\% & 48\% & 61\% \\ \hline Allowed & - & 53\% & 52\% & 58\% & 56\% & 52\% & 39\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The percentage of all allowed and blocked external JavaScript programs. Figure 4: The total number of JavaScript programs extracted from the 50 domains. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**JSec** & **PPTs Off** & **DC** & **GT** & **ABP** & **UB** & **PB** & **NS** \\ \hline In-page & 828 & 467 & 691 & 590 & 646 & 496 & 698 \\ \hline Blocked & - & 40\% & 11\% & 24\% & 17\% & 36\% & 11\% \\ \hline Allowed & - & 60\% & 89\% & 76\% & 83\% & 64\% & 89\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The percentage of all allowed and blocked in-page JavaScript programs. where True Positive (TP) is the number of functional JavaScript programs that are correctly identified, False Negative (FN) is the number of tracking JavaScript programs that are incorrectly allowed. While Specificity is defined as the probability of correctly rejecting the existence of a particular feature or condition. This is also known as the true negative rate (TNR).