## 3 The Dataset  
The dataset used in this work, published by [PERSON] and [PERSON] and [PERSON] (2018), was originally retrieved from the Privacy Rights Clearinghouse repository. The authors subsequently filtered the PRC dataset to encompass data breach events that impacted companies listed on the NYSE or NASDAQ exclusively. Therefore, the work of [PERSON] and [PERSON] and [PERSON] (2018) published and described the dataset, which this study analyses and discusses.  
According to PRC, the data breaches were primarily gathered from U.S. Attorneys General and the Department of Health and Human Services, and they do not constitute an exhaustive list of all breaches, reflecting only the data breaches that have been reported  
\begin{table}
\begin{tabular}{c p{341.4 pt}} \hline \hline
**Reference** & **Description** \\ \hline [PERSON] and [PERSON] (2018) & Assesses the effects of cybersecurity investments within the realm of data breaches. \\ \hline \multirow{2}{*}{[PERSON] and [PERSON] (2018)} & Explores the connection between cybersecurity performance influencing factors that lead to human errors and the subsequent occurrence of data breaches. \\ \hline \multirow{2}{*}{[PERSON] and [PERSON] (2018)} & Investigates the impact of non-financial disclosure laws obligations to breached companies. \\ \cline{2-3}  & Investigates the responses of organizations to data breach incidents. \\ \cline{2-3}  & Presents statistical distributions of malicious and negligent data breaches. \\ \cline{2-3}  & Assesses the implications of implementing an inverted firm strategy with the use of Application Programming Interfaces (APIs), highlighting the elevated risk of data breaches associated with the use of public APIs. \\ \hline \multirow{2}{*}{[PERSON] and [PERSON] (2018)} & Proposes a cybersecurity risk-quantification-and-classification framework designed for application to real-world data breaches. \\ \cline{2-3}  & Examines the compliance strategies that IT business leaders within nonprofit organizations should adopt to reduce the risk of cyber threats that could potentially lead to data breaches. \\ \cline{2-3}  & Reviews predictive algorithms and analyzes trends of breach incidents. \\ \cline{2-3}  & Performs a brief exploratory data analysis of the dataset. \\ \cline{2-3}  & Studies the effectiveness of K-Means Featurization (KMF) in addressing the challenges presented by complex datasets. \\ \cline{2-3}  & Examines how earnings disclosures influence cyberattackers’ behavior. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Publications that cited the work of [PERSON] and [PERSON] (2018).  
and made publicly available in the United States. The list is even less exhaustive in the case of this work, as it comprehends publicly listed U.S. companies exclusively. Nevertheless, the dataset was deemed suitable for the objectives of this study, which were to estimate breach sizes and explore various security facets related to data breaches within NYSE- and NASDAQ-listed companies.  
The dataset is presented in a tabular format, in which each column contains specific information about these events. In total, the table comprises 506 rows, each representing a data breach, and 15 columns. To enhance the analysis conducted, the dataset was enhanced with information regarding the economic sector in which each company operates, using Yahoo! Finance API. This augmentation allowed for a more comprehensive examination of the factors pertinent to data breaches. However, for this paper, only nine of the original columns were used, with the addition of the economy sector column. A description of these 10 dataset fields is presented in Table 2.