# AI-Based Analysis of Policies and Images for Privacy-Conscious Content Sharing
## 3 Information Retrieval for Privacy Policies
### Classification of Privacy Practices  
As explained before, each paragraph may contain no privacy practice, or a privacy practice referring to one or more data types. As a consequence, the problem we are tackling is a multi-label classification problem, in which the label/classes correspond to the data types.  
In the literature, multi-label problems have been addressed in different ways [25]. A straightforward approach is to transform the multi-label problem into a single-label multi-class classification problem, where the classes are all the possible label combinations. However, for the sake of this work, we consider the 30 classes (i.e., data types); hence, with this approach, the number of possible classes would be 230 = 1,073,741,824. Since the number of classes would be unfeasible, we discarded this approach.  
In our work, we used the so-called _binary relevance method_, which is widely used in the literature [26]. Hence, we decomposed the multi-label problem into multiple binary classification tasks, one for each data type. Every data type classifier uses two classes: 'yes', meaning the presence of a privacy practice description regarding that data type; and 'no' meaning the opposite. In Table 1, we list the considered data types, each corresponding to a binary classifier. For the sake of this paper, we used a random forest classifier, since it is generally effective for text classification [27].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Data Type** & **Description** & **Example** \\ \hline Contact & User’s generic contact data privacy practices & We collect some contact information from you \\ \hline Contact address book & Regarding the user’s phone address book & We could access to your contact list such as phone address book \\ \hline Contact city & User’s city & We collect some contact information such as your city \\ \hline Contact email address & User’s email address & When you subscribe, you provide us some of your personal information such as your email \\ \hline Contact password & User’s password & When you subscribe, you provide us some of your personal information such as your account password \\ \hline Contact phone number & User’s phone number & When you subscribe to our service, you provide us some of your personal information such as your phone number \\ \hline Contact postal address & User’s postal address & We collect some contact information such as your postal address \\ \hline Contact ZIP & User’s ZIP (Zoning Improvement Plan) code & We collect some contact information such as your ZIP code \\ \hline Demographic & User’s unspecified demographic data & We collect some demographic information from you \\ \hline Demographic age & User’s age & We collect some demographic information from you such as your age \\ \hline Demographic gender & User’s gender & We collect some demographic information from you such as your gender \\ \hline Identifier & Unspecified identifiers & We may collect usage information; this can include some device identifiers’ information \\ \hline Identifier ad ID & Identifier for advertising & We could collect some advertising identifier information such as your Google ad ID (gaid) \\ \hline Identifier cookie or similar tech & Cookies, pixel tags, etc. & Cookies are unique identifiers that we transfer to your device to enable our systems to recognize your device \\ \hline Identifier device ID & Device identifiers & We could collect some device identifier information such as your device IMEI code \\ \hline \hline \end{tabular}
\end{table}
Table 1: Privacy practice data types.