  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 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.
