# Detecting Personally Identifiable Information Through Natural Language Processing: A Step Forward
## 2 Related Work and Reasons to Continue Research in PII Detection  
### Essential Literature Review  
Over the years, corporate companies, healthcare industries, and other public companies have become prime targets for attackers seeking PII, in particular, exploiting the vulnerabilities of social networks, mobile apps, and mobile networks [4]. As an example, more than ten years ago, [PERSON] et al. [5] proposed a method to automatically detect PII in internet traffic using a combination of systematic expressions (such as regular expressions) and dictionary-based methods. As an improvement to the previous work, in 2019, [PERSON] et al. [6] introduced a Software-Defined Networking (SDN) and Network Function Virtualization (NFV)-enabled architecture to address the unintended disclosure of PII over network traffic, particularly on social media platforms. This research is highly relevant in today's environment, where both privacy breaches and data leaks on the internet, especially on social networks, are a significant concern. A study by [PERSON] et al. [7] introduced a system called ReCon, which aims to tackle the issue of PII leakage in mobile network traffic. By using a supervised learning model, ReCon effectively identifies and blocks unintended disclosure of PII that might be transmitted over mobile networks. As another application of learning techniques, in 2020, [PERSON] (2020) proposed a novel approach to identifying persons of interest (PoI) and PII within the context of corporate email datasets--specifically, the Enron Corpus. The study leverages ensemble learning techniques, including decision trees, support vector classifiers (SVCs), random forests, and neural networks, to analyze textual data from corporate emails and financial records, extracting both PII and PoI while also analyzing employee sentiment during a corporate crisis. Furthermore, the authors use techniques with multiple classifiers (as in ([PERSON] et al., 2017)) to optimize performance. In their work, [PERSON] et al. (2017) focused on the critical issue of PII in the context of public health research, specifically when utilizing online geographic tools and big data tools. Their study addresses the potential risks associated with the handling and sharing of sensitive health data, particularly when such data are geographically tagged or integrated into public health analysis. Similarly, [PERSON] et al. (2017) explored the issue of protecting PII in the context of healthcare data, particularly focusing on critical infrastructure data (see ([PERSON] et al., 2017)) and emphasizing innovative methods to balance privacy protection with the need for efficient data analysis. The study examines differential privacy, a prominent privacy-preserving technique, and compares it with other traditional anonymization techniques to protect PII in healthcare data. The authors provide a novel approach for protecting PII in large datasets, demonstrating that multiple attribute workload distribution can be more effective than traditional anonymization and differential privacy techniques. [PERSON] et al. (2018) addressed the protection of PII on mobile devices. The work introduces an intelligent risk classification model based on the permissions requested by Android applications to classify vulnerable PII associated with mobile device owners. By analyzing app permissions, the model helps to assess the risks of exposing sensitive data, such as contact numbers, social graph data, emails, location data, biometric identifiers, and unique device IDs. [PERSON] et al. (2018) proposed an innovative approach to anonymizing PII, with a dual focus on privacy and data utility. The goal of their work is to develop a method that protects user privacy by anonymizing PII while maintaining the utility of the data for subsequent analysis or publication. The proposed scheme considers both vulnerability and diversity as important factors in the anonymization process. [PERSON] et al.