Machine Learning based Stochastic Techniques for Collaborative Privacy
A Case Study in Social Recommender Services
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This study focuses primarily on social recommender services which are of great interest. On the one hand, they lay the groundwork for new innovative applications but on the other hand, they pose numerous unique challenges to privacy. We studied the privacy problem faced by people in sharing their profiles' preferences within various scenarios of social recommender services. We proposed and developed a collaborative privacy approach for preserving users' profile privacy and we have applied this approach to representative scenarios. We discussed how our approach could handle the privacy problem ...
This study focuses primarily on social recommender services which are of great interest. On the one hand, they lay the groundwork for new innovative applications but on the other hand, they pose numerous unique challenges to privacy. We studied the privacy problem faced by people in sharing their profiles' preferences within various scenarios of social recommender services. We proposed and developed a collaborative privacy approach for preserving users' profile privacy and we have applied this approach to representative scenarios. We discussed how our approach could handle the privacy problem in these scenarios. In Addition, the proposed collaborative privacy framework was developed as a middleware that hosts a set of components to execute a two stage concealment process with novel stochastic techniques. Each stage in the two stage concealment process is carried out by completely different parties depending on their role in the coalition. This kind of approach is quite flexible and can easily be adopted in conventional social recommender services because it is executed on the user side and takes advantage of the social structure that is offered by the online social services.
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