Secure User Profile Matching in Social Networks

ABSTRACT:

Profile (e.g., contact list, interest, mobility) matching is more than important for fostering the wide use of mobile social networks. The social networks such as Facebook, Line or Wechat recommend the friends for the users based on users personal data such as common contact list or mobility traces. However, outsourcing users’ personal information to the cloud for friend matching will raise a serious privacy concern due to the potential risk of data abusing. In this study, we propose a novel Scalable and Privacy-preserving Friend Matching protocol, or SPFM in short, which aims to provide a scalable friend matching and recommendation solutions without revealing the users personal data to the cloud. Different from the previous works which involves multiple rounds of protocols, SPFM presents a scalable solution which can prevent honest-but-curious mobile cloud from obtaining the original data and support the friend matching of multiple users simultaneously. We give detailed feasibility and security analysis on SPFM and its accuracy and security have been well demonstrated via extensive simulations. The result show that our scheme works even better when original data is large.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM:

The existing mobile social network systems pay little heed to the privacy concerns associated with friend matching and recommendation based on users’ personal information. For example, in Facebook, it provides the feature of People You May Know, which recommends the friends based on the education information, the contact lists obtained from users’ smartphone, and other users’ personal information.
Li et al. applies additive homomorphic encryption in privacy preserving in a scenario with many intermediate computing parties.
Narayanan et al. and Dong et al. computes social proximity to discover potential friends by leveraging both homomorphic cryptography and obfuscation, which is more efficient.

DISADVANTAGES OF EXISTING SYSTEM:

Outsourcing users’ personal information to the cloud for friend matching will raise a serious privacy concern
Existing researches show that loss of privacy can expose users to unwanted advertisement and spams/scams, cause social reputation or economic damage, and make them victims of blackmail or even physical violence
The existing works may fail to work in practice due to the following two reasons. Firstly, the best practice in industry for friends recommendation is a multiple-users matching problem rather than a two-party matching problem. Some pre-share parameters between users are more likely to leak. Secondly, most of the existing works involve multiple rounds of protocols, which will suffer from a serious performance challenge.

PROPOSED SYSTEM:

In this study, we propose a novel Scalable and Privacy preserving Friend Matching protocol, or SPFM in short, which aims to provide a scalable friend matching and recommendation solutions without revealing the users personal data to the cloud.
Our basic motivation is that each user obfuscates every bit of the original personal data (e.g., contact list) before uploading by performing XOR operations with a masking sequence which is generated with a certain probability.
We propose a Scalable and Privacy-preserving Friend Matching scheme (SPFM) to prevent privacy leakage in friend matching and recommendation system.

ADVANTAGES OF PROPOSED SYSTEM:

Our design can ensure that the same data maintain a statistical similarity after obfuscation while different data can be statistically classified without leaking the original data.
We provide a detailed feasibility and security analysis as well as the discussion of correctness, True-Negative rate and True-Positive rate.
Extensive evaluations have been performed on SPFM to demonstrate the feasibility and security. The result show that our scheme works even better when original data is large.

MODULES:

System Framework
Users
Cloud Server

MODULES DESCRIPTION:

System Framework:

In this framework, we designed a novel Scalable and Privacy-preserving Friend Matching protocol or SPFM in short, which aims to provide a scalable friend matching and recommendation solutions without revealing the user’s personal data to the cloud. Different from the previous works which involves multiple rounds of protocols, SPFM presents a scalable solution which can prevent honest-but-curious mobile cloud from obtaining the original data and support the friend matching of multiple users simultaneously. We give detailed feasibility and security analysis on SPFM and its accuracy and security have been well demonstrated via extensive simulations.

Users:

In Users module, initially user must have to register their detail on the cloud server. After successful registration user can search their friends and also user can able to view the recommended friends in the recommended friend section. User can send friend request to known users and response the request in notification module. User can post a status update and that will appear on newsfeeds section. When the users post a status their geo location will add on the status. Others users comment on the post update by a user

Cloud Servers:

In Cloud server module, he/she can view all the user’s details in the database. Users details are in encrypted format so that user’s personal details are preserved in secured manner. He/she can view all the status posted by the users and posted location attached on that. Cloud server can view the user’s details by classifications like user’s interest, hobbies, location. Cloud server will able to see the graphical analyze of user details by interest, hobbies, and locations.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor
Hard Disk : 500 GB..
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
RAM : 4 GB.

SOFTWARE REQUIREMENTS:

Operating system : Windows 10/11.
Coding Language : C#.net.
Frontend : ASP.Net, HTML, CSS, JavaScript.
IDE Tool : VISUAL STUDIO.
Database : SQL SERVER 2005.

Dotnet Projects

Secure User Profile Matching in Social Networks