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The filter bubble is a concept developed by Internet activist Eli Pariser in his book "Filter Bubble" to describe a phenomenon in which websites use algorithms to predict what information a user may like to see based on the user's location, search history, etc. As a result, a website may only show information which agrees with the user's past viewpoints. A typical example is Google's personalized search results. To "pop" the bubbles created by Google search (also called de-personalization), our research group in the Georgia Tech Information Security Center is conducting ground-breaking research and developing software, Filter Bubble. Filter Bubble is a chrome extension that uses hundreds of nodes to distribute a user's Google search queries world wide each time the user performs a Google search. Using Filter Bubble, a user can easily see differences between his and others' Google search returns.