In the past few years, the growing number of personal information shared on the Web (through Web ... more In the past few years, the growing number of personal information shared on the Web (through Web 2.0 applications) increased awareness regarding privacy and personal data. Recent studies showed that privacy in Social Networks is a major concern when user profiles are publicly shared, revealing that most users are aware of privacy settings. Most Social Networks provide privacy settings restricting access to private data to those who are in the user’s friends lists (i.e. their “social graph”) such as Facebook’s privacy preferences. Yet, the studies show that users require more complex privacy settings as current systems do not meet their requirements. Hence, we propose a platform-independent system that allows end-users to set fine-grained privacy preferences for the creation of privacy-aware faceted user profiles on the Social Web.
Abstract. With the rapid growth in users on social networks, there is a corresponding increase in... more Abstract. With the rapid growth in users on social networks, there is a corresponding increase in user-generated content, in turn resulting in information overload. On Twitter, for example, users tend to receive uninterested information due to their non-overlapping interests from the people whom they follow. In this paper we present a Semantic Web approach to filter public tweets matching interests from personalized user profiles. Our approach includes automatic generation of multi-domain and personalized user profiles, ...
In the past few years, the growing number of personal information shared on the Web (through Web ... more In the past few years, the growing number of personal information shared on the Web (through Web 2.0 applications) increased awareness regarding privacy and personal data. Recent studies showed that privacy in Social Networks is a major concern when user profiles are publicly shared, revealing that most users are aware of privacy settings. Most Social Networks provide privacy settings restricting access to private data to those who are in the user’s friends lists (i.e. their “social graph”) such as Facebook’s privacy preferences. Yet, the studies show that users require more complex privacy settings as current systems do not meet their requirements. Hence, we propose a platform-independent system that allows end-users to set fine-grained privacy preferences for the creation of privacy-aware faceted user profiles on the Social Web.
Abstract. With the rapid growth in users on social networks, there is a corresponding increase in... more Abstract. With the rapid growth in users on social networks, there is a corresponding increase in user-generated content, in turn resulting in information overload. On Twitter, for example, users tend to receive uninterested information due to their non-overlapping interests from the people whom they follow. In this paper we present a Semantic Web approach to filter public tweets matching interests from personalized user profiles. Our approach includes automatic generation of multi-domain and personalized user profiles, ...
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Papers by F. Orlandi