Data anonymization and de-identification techniques
Table Of Contents
<p>1. Introduction<br> 1.1 Background and Motivation<br> 1.2 Objectives of the Project<br>2. Privacy Preservation and Data Anonymization<br> 2.1 Legal and Regulatory Frameworks<br> 2.2 Principles of Data De-identification<br>3. Anonymization Techniques for Structured Data<br> 3.1 Generalization and Suppression Methods<br> 3.2 K-anonymity and L-diversity Models<br>4. De-identification of Unstructured Data<br> 4.1 Text Redaction and Tokenization<br> 4.2 Differential Privacy and Perturbation Techniques<br>5. Utility and Information Loss Analysis<br> 5.1 Quantitative Measures of Anonymization<br> 5.2 Trade-offs between Privacy and Data Utility<br></p>
Thesis Abstract
<p> This project aims to investigate data anonymization and de-identification techniques for preserving privacy and confidentiality in sensitive datasets. The project will explore various methods for removing or obfuscating personally identifiable information (PII) from structured and unstructured data while retaining the utility and integrity of the information for analysis and research purposes. The project will also address the legal and ethical considerations associated with data anonymization practices. <br></p>Thesis Overview
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