# An Effective Grouping Method for Privacy-Preserving Bike Sharing Data Publishing
## 4 Experimental Analysis
### Disclosure  
At the time of bike sharing transaction table publishing, the data publisher releases the dataset in a raw format by removing identifiers. To calculate the disclosure risks in the bike sharing dataset, we count all the records as the \(Ql\) values. We group the vulnerable \(Ql\) values based on their birth year,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Attribute** & **Type** & **Number of Unique Values** \\ \hline
1 & Birth Year & Continuous & 87 \\
2 & Gender & Categorical & 2 \\
3 & Start Station & Categorical & 475 \\
4 & End Station & Categorical & 483 \\
5 & Start Time & Date & 2,331,112 \\
6 & End Time & Date & 2,330,582 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Data set description of bike sharing transaction table.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Birth Year** & **Gender** & **(Start Station, End Station)** & **(Start Time, End Time)** \\ \hline
1983 & Person & (E 20 St \& 2 Ave, Broadway \& E 22 St) & (4/7/2016 6:49, 4/7/2016 6:55) \\
1988 & Person & (E 47 St \& Park Ave, W 49 St \& Ave) & (4/20/2016 8:41, 4/20/2016 8:45) \\
1970 & Person & (8 Ave \& W 52 St, W 63 St \& Broadway) & (4/20/2016 11:16, 4/20/2016 11:20) \\
1988 & Person & (Carlton Ave \& Flushing Ave, Front St \& Gold St) & (4/7/2016 18:06, 4/7/2016 18:11) \\
1981 & Person & (E 47 St \& Park Ave, W 49 St \& Ave) & (4/7/2016 6:47, 4/7/2016 6:52) \\
1970 & Person & (Broadway \& E 22 St, E 20 St \& 2 Ave) & (4/1/2016 6:53, 4/1/2016 6:59) \\ \hline
1986 & Person & (E 17 St \& Broadway, E 27 St \& 1 Ave) & (4/5/2016 13:48, 4/5/2016 13:56) \\
1970 & Person & (W 49 St \& Ave, E 47 St \& Park Ave) & (4/7/2016 17:56, 4/7/2016 18:02) \\
1983 & Person & (W 49 St \& Ave, E 47 St \& Park Ave) & (4/1/2016 16:12, 4/1/2016 16:19) \\
1986 & Person & (W 21 St \& 6 Ave, E 20 St \& 2 Ave) & (4/21/2016 7:53, 4/21/2016 8:00) \\
1970 & Person & (E 27 St \& 1 Ave, E 17 St \& Broadway) & (4/6/2016 016: 4/6/2016 02:25) \\
1986 & Person & (E 27 St \& 1 Ave, E 17 St \& Broadway) & (4/20/2016 0:06, 4/20/2016 0:15) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Anonymization table of bike sharing transaction table (Grouping).  
gender, start locations and end locations. Figure 2 shows the representation of datasets with unique \(Ql\) values and probable disclosure risks from January 2016 to May 2016. The primary objective of the anonymization technique continues to break the correlation between these \(Ql\) values to ensure personal privacy.  
We further calculate the disclosure risks in the published anonymized dataset. To calculate the disclosure risks, we compare the original dataset \(Ql\) values with the anonymized dataset \(Ql\) values. If a record in the anonymized dataset matches the original dataset \(Ql\) values, then we count it as disclosed \(Ql\) values. Disclosure risks are calculated by the following formula [59]:  
\[Disclosure=\frac{Matched\ records}{Total\ records}\times 100\%.\]  
Figure 3 shows disclosure risks for the _k-anonymity_[13] and the Grouping method. On the _x_-axis, it shows datasets from January to May and on the _y_-axis represents the corresponding disclosure risks. The experimental results show that the Grouping method has less disclosure risks than the _k-anonymity_ method: