Mobile recommender systems: An overview of technologies and challenges <s> V. FUTURE WORK <s> Privacy is an enormous problem in online social networking sites. While sites such as Facebook allow users fine-grained control over who can see their profiles, it is difficult for average users to specify this kind of detailed policy. In this paper, we propose a template for the design of a social networking privacy wizard. The intuition for the design comes from the observation that real users conceive their privacy preferences (which friends should be able to see which information) based on an implicit set of rules. Thus, with a limited amount of user input, it is usually possible to build a machine learning model that concisely describes a particular user's preferences, and then use this model to configure the user's privacy settings automatically. As an instance of this general framework, we have built a wizard based on an active learning paradigm called uncertainty sampling. The wizard iteratively asks the user to assign privacy "labels" to selected ("informative") friends, and it uses this input to construct a classifier, which can in turn be used to automatically assign privileges to the rest of the user's (unlabeled) friends. To evaluate our approach, we collected detailed privacy preference data from 45 real Facebook users. Our study revealed two important things. First, real users tend to conceive their privacy preferences in terms of communities, which can easily be extracted from a social network graph using existing techniques. Second, our active learning wizard, using communities as features, is able to recommend high-accuracy privacy settings using less user input than existing policy-specification tools. <s> BIB001 </s> Mobile recommender systems: An overview of technologies and challenges <s> V. FUTURE WORK <s> For purposes of this paper, we define "Personally identifiable information" (PII) as information which can be used to distinguish or trace an individual's identity either alone or when combined with other information that is linkable to a specific individual. The popularity of Online Social Networks (OSN) has accelerated the appearance of vast amounts of personal information on the Internet. Our research shows that it is possible for third-parties to link PII, which is leaked via OSNs, with user actions both within OSN sites and elsewhere on non-OSN sites. We refer to this ability to link PII and combine it with other information as "leakage". We have identified multiple ways by which such leakage occurs and discuss measures to prevent it. <s> BIB002
Current models provide practically no protection for privacy and data security with a clear example being Facebook that requires to associate users' personal information with their id BIB001 BIB002 . Recommender systems in mobile devices should use a trusted centralized server in order to achieve privacy. This can be succeeded with the use of a modified Role-Based Access Control model and the trusted platform module. The RBAC model needs to be extended to provide enhanced security both in the permissions of the mobile applications and the security of the data itself in the server. Cloud computing is another feature that should be taken into consideration. Cloud computing platforms can be used to store user profile information. This information can then be joined with the mobile device to improve the user experience and provide a low-cost environment. Moreover, this carries privacy and security concerns that can be resolved with the use of the extended RBAC model.