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:-

ü Processor          -   Pentium –III

ü Speed                -    1.1 Ghz

ü RAM                 -    256 MB(min)

ü Hard Disk         -   20 GB

ü Floppy Drive    -    1.44 MB

ü Key Board                 -    Standard Windows Keyboard

ü Mouse               -    Two or Three Button Mouse

ü Monitor             -    SVGA

SOFTWARE REQUIREMENTS:-

v  Operating System      : Windows 7    

v  Front End                  :ASP.net and C#

v  Database                    : MSSQL

v  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.

Selcsp: A Framework To Facilitate Selection Of Cloud Service Providers

Abstract

With rapid technological advancements, cloud marketplace witnessed frequent emergence of new service providers with similar offerings. However, service level agreements (SLAs), which document guaranteed quality of service levels, have not been found to be consistent among providers, even though they offer services with similar functionality. In service outsourcing environments, like cloud, the quality of service levels are of prime importance to customers, as they use third-party cloud services to store and process their clients’ data. If loss of data occurs due to an outage, the customer’s business gets affected. Therefore, the major challenge for a customer is to select an appropriate service provider to ensure guaranteed service quality. To support customers in reliably identifying ideal service provider, this work proposes a framework, SelCSP, which combines trustworthiness and competence to estimate risk of interaction. Trustworthiness is computed from personal experiences gained through direct interactions or from feedbacks related to reputations of vendors. Competence is assessed based on transparency in provider’s SLA guarantees. A case study has been presented to demonstrate the application of our approach. Experimental results validate the practicability of the proposed estimating mechanisms

Aim

The aim of this paper proposes a framework, SelCSP, which combines trustworthiness and competence to estimate risk of interaction.

Scope:

The scope of this paper tends to validate the practicability of the proposed estimating the risk of interaction.

Existing System

In most cases, it has been observed that the failover time is quite long and customers’ businesses were hugely affected owing to lack of recovery strategy on vendor side. Moreover, in some instances, customers were not even intimated about the outage by providers. Cloud providers may use the high-quality first replication (HQFR) strategy proposed in to model their recovery mechanism. In this work, authors propose algorithms to minimize replication cost and the number of QoS-violated data replicas. Hence, it is desirable from customer’s point of- view to avoid such loss, rather than getting guarantees of service credits following a cloud outage. Avoidance of data loss requires reliable identification of competent service provider. As customer does not have control over its data deployed in cloud, there is a need to estimate risk prior to outsourcing any business onto a cloud. This motivated us to propose a risk estimation scheme which makes a quantitative assessment of risk involved while interacting with a given service provider.

 Disadvantages

 Lack of assurances and violations for SLA guarantees

 Multi-tenancy, lack of customer’s control over their data and application

Non-transparency with respect to security profiles of remote datacenter locations

Proposed System

In this paper estimation of risk of interaction in cloud environment has not been addressed. Hence, in this respect, the current work is significant as it proposes a framework, SelCSP , which attempts to compute risk involved in interacting with a given cloud service provider. The framework estimates perceived level of interaction risk by combining trustworthiness and competence of cloud provider. Trustworthiness is computed from ratings obtained through either direct interaction or feedback.

Advantages

1. The framework estimates trustworthiness in terms of context-specific, dynamic trust and reputation feedbacks.
2. Both these entities are combined to model interaction risk, which gives an estimate of risk level involved in an interaction

 System Architecture

SYSTEM CONFIGURATION

HARDWARE REQUIREMENTS:-

·                Processor          -   Pentium –III

·                Speed                -    1.1 Ghz

·                RAM                 -    256 MB(min)

·                Hard Disk         -   20 GB

·                Floppy Drive    -    1.44 MB

·                Key Board                 -    Standard Windows Keyboard

·                Mouse               -    Two or Three Button Mouse

·                Monitor             -    SVGA

SOFTWARE REQUIREMENTS:-

·                Operating System      : Windows  7                                     

·                Front End                  : ASP.NET and C#

·                Database                   : MSSQL

·                Tool                           :Visual Studio

References

Ghosh, N.,Ghosh, S.K.,  Das, S.K. “SelCSP: A Framework to Facilitate Selection of Cloud Service Providers” IEEE Transactions on Cloud Computing, Volume 3 ,  Issue 1 JULY 2014.