A Human-Machine Approach to Preserve Privacy in Image Analysis Crowdsourcing Tasks

A Human-Machine Approach to Preserve Privacy in Image Analysis Crowdsourcing Tasks

Shriram, Sharad (TU Delft Electrical Engineering, Mathematics and Computer Science)

Bozzon, Alessandro (mentor)
Mauri, Andrea (mentor)
Houben, Geert-Jan (graduation committee)
Finavaro Aniche, Mauricio (graduation committee)

Delft University of Technology

Computer Science

2019-08-19

Modern web information systems use machine learning models to provide personalized user services and experiences. However, machine learning models require annotated data for training, and creating annotated data is done through crowdsourcing tasks. The content used in annotation crowdsourcing tasks like medical records and images might contain some private information which can directly or indirectly identify an individual. The name, age, ethnicity, gender, contact details are examples of private information that directly identifies an individual. Indirect private information relates to the cultural, economic, and social factors of an individual. For instance, the visual cues of religious objects or symbols relate to the religious beliefs of an individual. In this thesis, we study how to minimize the amount of private information extracted from images using a hybrid algorithm which combines machine learning models and crowdsourcing. We also demonstrate that the proposed hybrid algorithm reduces the amount of private information exposed from the image and the cost of using the crowd for detecting private information in the image.

Privacy-preserving
Crowdsourcing
Machine Learning
Hybrid Human-AI Algorithm
Hybrid Intelligence
Privacy

http://resolver.tudelft.nl/uuid:30846529-9080-4945-8502-dc962ec00bf3

Student theses

master thesis

© 2019 Sharad Shriram