Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
WMR--A Graph-Based Algorithm for Friend Recommendation
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Make new friends, but keep the old: recommending people on social networking sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
On the leakage of personally identifiable information via online social networks
ACM SIGCOMM Computer Communication Review
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
User's privacy in applications provided through social networks
Proceedings of second ACM SIGMM workshop on Social media
Potential Friend Recommendation in Online Social Network
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Social Network Analysis and Mining for Business Applications
ACM Transactions on Intelligent Systems and Technology (TIST)
Personalized social recommendations: accurate or private
Proceedings of the VLDB Endowment
SFViz: interest-based friends exploration and recommendation in social networks
Proceedings of the 2011 Visual Information Communication - International Symposium
A Framework of Recommendation System Based on Both Network Structure and Messages
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
Stalking online: on user privacy in social networks
Proceedings of the second ACM conference on Data and Application Security and Privacy
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The emerging growth of online social networks have opened new doors for various business applications such as promoting a new product across its customers. Besides this, friend recommendation is an important tool for recommending potential candidates as friends to users in order to enhance the development of the entire network structure. Existing friend recommendation methods utilize social network structure and/or user profile information. However, these techniques can no longer be applicable if the privacy of users is taken into consideration. In this paper, we propose a two-phase private friend recommendation protocol for recommending friends to a given target user based on the network structure as well as utilizing the real message interaction between users. Our protocol computes the recommendation scores of all users who are within a radius of h from the target user in a privacy preserving manner. In addition, we show the practical applicability of our approach through empirical analysis.