# Efficient Multi-Source Anonymity for Aggregated Internet of Vehicles Datasets
## 3 Models
### Motivation  
The aggregated datasets formed in the IoV scenario can result in a new privacy problem. We begin by outlining the representation of aggregated datasets. Within IoV scenarios, data play a crucial role in supporting users through features such as automatic driving, navigation, and intelligent transportation systems. In the utilization of data, IoV services may incorporate personal information (e.g., location, behavioral patterns, and personal profiles), which could inadvertently include details about third parties, such as private properties. Additionally, IoV encompasses a variety of sensors, such as global positioning systems, radar, and cameras. Consequently, numerous aggregated datasets have been generated through the integration of data from multiple sources. First, we describe the creation of aggregated datasets. Typically, data obtained from various devices can be utilized directly or can undergo aggregation before use. The former scenario does not lead to the formation of aggregated datasets, whereas the latter involves concatenating pertinent information about individuals, potentially leading to the exposure of sensitive details. We delve into the most straightforward case in the aggregated scenario, for which the data are from distinct sensing devices or associated databases. Figure 2 illustrates the process of publishing aggregated datasets, which can be categorized into three phases. During the data collection stage, data are gathered from different sensing devices or related databases. In the data aggregation stage, vehicle information, personal profiles, and location information are combined for publication, with each record containing information about an individual. Finally, the aggregated dataset may be released to a data analyzer or online servers for purposes such as data mining and decision making. A common practice is to anonymize private information during this stage to obfuscate sensitive information regarding the identities of individuals. \begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(T\) & Original Dataset \\ \(AT\) & Aggregated Data \\ \(AT^{\prime}\) & Anonymized Aggregation Data \\ \(k\) & K-Anonymity Parameter \\ \(r\) & Record \\ \(A\) & Attribute \\ \(v\) & Number of Attributes \\ \(n\) & Number of Records \\ \(NCP\), _GCP_ & Information Loss Metric Function \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Symbols and descriptions. Figure 3 illustrates an aggregated dataset in the data aggregation phase; the aggregated dataset encompasses attributes such as license plates, registration dates, and colors. Here, the license plate is an EI, the address is an SA, and the others are QIs. For anonymization, the \(E\)Is are removed to prevent them from providing identification information that could reveal the unique vehicle owner. The presence of \(E\)Is is a potential source of identity disclosures. Similarly, \(Q\)Is such as registration date and color, which can individually identify a unique vehicle, need to be transformed into anonymous results. For instance, values such as [2017, White, 110/30,...] may be transformed into {[2017-2018, White, [110/30, 112/28],...]. Figure 3 provides an example of 2-anonymity and 2-diversity for the original and anonymized datasets, respectively. In addition, a set of records sharing the same \(Q\)I values constitutes an equivalence class. Failure to properly anonymize \(E\)Is or \(Q\)Is may result in leakage of sensitive values. For instance, if the sensitive attribute is the address, and two records within the same equivalence class have the same anonymized \(Q\)I values (e.g., No. 20 M Street), this could lead to attribute disclosure. **Definition 1** (**Quasi-Identifier Attribute)**. : _A quasi-identifier set \(QI\) is a minimal set of attributes in original dataset \(T\) that can be joined with external information to re-identify individual record \(r\)._  
**Definition 2** (**Equivalence Class)**.