Distance-based location privacy protection in social networks

The current privacy protection methods adopted by social network providers rely on restricting users' access rights. They force a user to rigidly divide other users into two categories only: friends and strangers. Based on this classification, they prevent non-friends from accessing to the user's data while provide full access for friends regardless of how close they are to her. However, the level of privacy protection can be increased gradually and smoothly rather than firmly like a zero/one function. Moreover, the utility of social networks is reduced if we prevent data miners from computing global statistics by applying rigid privacy policies. In this paper, we present a distance-based location privacy protection system (DBLP2) to preserve location privacy of social network users based on their friendship distance. Whenever a user wants to see another user's location (in her profile or from spatiotemporal tags on her posts), the system returns a differentially private response based on their friendship distance. In our proposed system, a user's location information provided for other users is more generalized as the friendship distance increases. In other words, family members and close friends receive a more accurate response than casual friends and strangers. Through analysis, we show that our proposed system makes the process of location privacy protection more flexible in terms of friendship distances.