Master's Thesis Felix Jedrzejewski
Privacy-Preserving Natural Language Processing: A Systematic Mapping Study
Abstract
This thesis aims to identify current trends in Privacy-Preserving Natural Language Processing (NLP) and potential research gaps that need to be explored. For this purpose, we will apply a systematic mapping study.
Motivation
Why is Privacy-Preserving NLP important? It protects our primary means of communication: Text and Speech. Nowadays, we exchange our data in different forms especially text and speech, for allegedly „free” service on third-party platforms. However, an eye-opening event for Privacy was the Cambridge Analytica/Facebook scandal showing what is done with our data we don't know about. In 2017, 2.6 billion records were breached (76% accidentally, 23% due to malicious outsiders). Data Breaches can lead to Identity Thefts, Credit Card Frauds, etc.
Research Questions
RQ1: What privacy-related challenges exist in the area of Natural Language Processing (NLP)?
RQ2: What approaches are used to preserve privacy in NLP tasks, and how can they be classified?
RQ3: What are the current research gaps and possible future research directions in the area of privacy-preserving NLP?
| Attribute | Value |
|---|---|
| Title (de) | Privatsphäre erhaltende Verarbeitung natürlicher Sprache: Eine systematische Mapping-Studie |
| Title (en) | Privacy-Preserving Natural Language Processing: A Systematic Mapping Study |
| Project | |
| Type | Master's Thesis |
| Status | completed |
| Student | Felix Jedrzejewski |
| Advisor | Alexandra Klymenko |
| Supervisor | Prof. Dr. Florian Matthes |
| Start Date | 15.12.2020 |
| Sebis Contributor Agreement signed on | 26.11.2020 |
| Checklist filled | Yes |
| Submission date | 15.07.2021 |