In the age of pervasive internet where people are communicating, networking, buying, paying bills, managing their health and finances over the internet, where sensors and machines are tracking real-time information and communicating with each other, it is but natural that big data will be generated and analyzed for the purpose of "smart business" and "personalization". Today storage is no longer a bottleneck and the benefit of analysis outweighs the cost of making user profiling omnipresent. However, this brings with it several privacy challengesrisk of privacy disclosure without consent, unsolicited advertising, unwanted exposure of sensitive information and unwarranted attention by malicious interests. We survey privacy risks associated with personalization in Web Search, Social Networking, Healthcare, Mobility, Wearable Technology and Internet of Things. The article reviews current privacy challenges, existing privacy preserving solutions and their limitations. We conclude with a discussion on future work in user controlled privacy preservation and selective personalization, particularly in the domain of search engines.
Web Search Engines are tools that help users find information. These search engines use the information provided by users, in terms of their search history to build their "user profiles". Rich user profiles enable the search engines to provide better personalized search results. However, this puts the user's privacy at risk. Apart from the risk of exposing one's identity, there is the added disadvantage of being subjected to unsolicited advertising and potential disclosure of sensitive information. Rich user profiles contain a lot of personally identifiable information, which can attract unwarranted malicious interests. It is important that sensitive data collection be curbed or at least obfuscated at the source. To that effect this work is a novel approach towards providing a balance between privacy preservation and personalization by keeping the user in control of his privacy Vs personalization decisions. This work supports complex queries and obfuscates them by adding a set of fake queries that are semantically related to the original query where both the semantic distance and the number of fake queries are user controlled parameters.
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