Data anonymization and de-identification techniques
Table Of Contents
- 1.Introduction
- 1.1Background and Motivation
- 1.2Objectives of the Project
- 2.Privacy Preservation and Data Anonymization
- 2.1Legal and Regulatory Frameworks
- 2.2Principles of Data De-identification
- 3.Anonymization Techniques for Structured Data
- 3.1Generalization and Suppression Methods
- 3.2K-anonymity and L-diversity Models
- 4.De-identification of Unstructured Data
- 4.1Text Redaction and Tokenization
- 4.2Differential Privacy and Perturbation Techniques
- 5.Utility and Information Loss Analysis
- 5.1Quantitative Measures of Anonymization
- 5.2Trade-offs between Privacy and Data Utility
Thesis Abstract
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.
Thesis Overview
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