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ThinkMind // ACHI 2023, The Sixteenth International Conference on Advances in Computer-Human Interactions // View article achi_2023_4_260_20121
Protecting User Privacy in Online Settings via Supervised Learning
 Authors:
Alexandru Rusescu
Brooke Lampe
Weizhi Meng Keywords: User Privacy; Supervised Learning; Support Vector Machine; Logistic Regression; Decision Tree Abstract:
Companies that have an online presence-in particular, companies that are exclusively digital-often subscribe to this business model: collect data from the user base, then expose the data to advertisement agencies in order to turn a profit. Such companies routinely market a service as "free" while obfuscating the fact that they tend to "charge" users in the currency of personal information rather than money. However, online companies also gather user data for more principled purposes, such as improving the user experience and aggregating statistics. The problem is the sale of user data to third parties. In this work, we design an intelligent approach to online privacy protection that leverages supervised learning. By detecting and blocking data collection that might infringe on a user's privacy, we can then restore a degree of digital privacy to the user. In our evaluation, we collect a dataset of network requests and measure the performance of several classifiers that adhere to the supervised learning paradigm. The results of our evaluation demonstrate the feasibility and potential of our approach. Pages: 228 to 234 Copyright: Copyright (c) IARIA, 2023 Publication date: April 24, 2023 Published in: conference ISSN: 2308-4138 ISBN: 978-1-68558-078-0 Location: Venice, Italy Dates: from April 24, 2023 to April 28, 2023
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