A Trust-based Privacy-Preserving Friend Recommendation Scheme for Online Social Networks

ABSTRACT

Online Social Networks (OSNs), which attract thousands of million people to use everyday, greatly extend OSN users’ social circles by friend recommendations. OSN users’ existing social relationship can be characterized as 1-hop trust relationship, and further establish a multi-hop trust chain during the recommendation process. As the same as what people usually experience in the daily life, the social relationship in cyberspaces are potentially formed by OSN users’ shared attributes, e.g., colleagues, family members, or classmates, which indicates the attribute-based recommendation process would lead to more fine-grained social relationships between strangers. Unfortunately, privacy concerns raised in the recommendation process impede the expansion of OSN users’ friend circle. Some OSN users refuse to disclose their identities and their friends’ information to the public domain. In this paper, we propose a trust based privacy-preserving friend recommendation scheme for OSNs, where OSN users apply their attributes to find matched friends, and establish social relationships with strangers via a multi-hop trust chain. Based on trace-driven experimental results and security analysis, we have shown the feasibility and privacy preservation of our proposed scheme.

AIM

The aim of this paper is OSN users apply their attributes to find matched friends, and establish social relationships with strangers via a multi-hop trust chain

SCOPE:

The Scope of this Paper is to trace-driven experimental results and security analysis, we have shown the feasibility and privacy preservation of our Trust Based Privacy Preserving Friend Recommendation Scheme.

EXISTING SYSTEM

On the one hand, directly asking recommendations to strangers or a non close friend not only reveals Alice’s identity, but also reveals her health condition and medical information. Even worse, traditional recommendation approaches applying identity to recommend strangers will disclose OSN users’ social relationships to the public, which impede patients from utilizing it, and also decrease the possibility of establishing the multi-hop trust chain if one of OSN users on the chain returns a negative result. On the other hand, current approaches cannot achieve the fine-grained and context-aware results automatically, due to the fact that OSN users have to determine the recommended friends based on their own judgments on the recommendation query. As in our example, Alice would like to ask for help from her friends who work in a hospital, but not a truck driver. To overcome the above issue, we consider the possibility of singsong users’ social attributes to establish the multi-hop trust chain based on each context-aware 1-hop trust relationship, where most of trust relationships are formed and strengthened by the shared social attributes.

DISADVANTAGES

1. Privacy  concerns raised in the recommendation process impede the expansion of OSN users’ friend circle
2. Some OSN users refuse to disclose their identities and their friend’s information to the public domain.

PROPOSED SYSTEM

In this paper, design a light-weighted privacy-preserving friend recommendation scheme for OSNs by utilizing both users’ social attributes and their existing trust relationships to establish a multi-hop trust chain between strangers. In our scheme, we jointly consider privacy leakages and preservation approaches regarding the identity, social attributes, and their trust relationships of OSN users during the recommendation process. By trace-driven experimental results, we demonstrate both the security and efficiency of our proposed scheme

ADVANTAGES

1. Based on the 1-hop trust relationships, we extend existing friendships to multi-hop trust chains without compromising recommenders identity privacy
2. Extensive trace-driven experiment are deployed to verify the performance of our scheme in terms of security, efficiency, and feasibility.

 SYSTEM ARCHITECTURE

 

 

SYSTEM CONFIGURATION:-

Hardware Requirements

• Speed                  -    1.1 Ghz
• Processor              -    Pentium IV
• RAM                    -    512 MB (min)
• Hard Disk            -    40 GB
• Key Board                    -    Standard Windows Keyboard
• Mouse                  -    Two or Three Button Mouse
• Monitor                -     LCD/LED

 Software requirements

• Operating System              : Windows 7             
•  Front End                           : ASP.Net and C#
• Database                             : MSSQL
• Tool                                    : Microsoft Visual studio

REFERENCE

Zhang, C Fang, Y.Guo, L.“ A Trust-based Privacy-Preserving Friend Recommendation Scheme for Online Social Networks,” IEEE Transactions on Dependable and Secure Computing, Volume 12 ,  Issue 4  , SEPTEMBER 2014.