Digital Privacy in Crowdsourcing

Bys: Kumar, Santosh and Faisal, Mohammad

The main aim of this study was to come up with an effective adaptive framework that could be able to predict and protect the digital privacy of the crowdsourcing environment. To do so, the research investigated behavioral factors that influence privacy choices around data privacy, applied machine learning models for predicting privacy status, and understood the characteristics that drive encryption behavior. The ultimate aim was to close the gap between technological innovation and user-oriented privacy protection in collaborative virtual ecosystems.

• Format: Paperback
• Language: English
• ISBN: 6630007735
• ISBN13: 9786630007732
• Release Date: June 2026
• Publisher: LAP Lambert Academic Publishing
• Length: 260 Pages
• Weight: 0.78 lbs.
• Dimensions: 0.6" x 6.0" x 9.0"