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A SYSTEM TO FILTER

UNWANTED MESSAGESFROM OSN USER WALLS

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INTRODUCTION

Online Social Networks (OSNs) are today one of the most

popular interactive medium to communicate, share, and disseminate a

considerable amount of human life information. Daily and continuous

communications imply the exchange of several types of content,

including free text, image, audio, and video data. In the sharing In

OSNs, information filtering can also be used for a different, more

sensitive, purpose. This is due to the fact that in OSNs there is the

possibility of posting or commenting other posts on particular

public/private areas, called in general walls. Information filtering can

therefore be used to give users the ability to automatically control themessages written on their own walls, by filtering out unwanted

messages.

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SCOPE OF THE PROJECT

The OSN have three layer there are graphical user interface,

social network application and social network managers. The social

network managers handle the basic functionalities like profile

management, network based function etc. But in this project focused on

other two layers and apply some new condition. Application layer have

short text classifier and content based message filtering. Short text

classifier classifying the messages based on the content. Content based

message filter have black list and filtering policies. First, find

relationship between the user and message senders and it will filter and

calculate the probabilities using classifier. And the send a empty messagebelow the probabilities result to the user. So our proposed system will

give the direct control to the user that what kind of messages displays on

their wall.

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LITERATURE SURVEY

Title: BoosTexter: A Boosting-based System for Text

CategorizationAuthor: Robert E Schapire, Yoram Singer

Year: 2000

We use adopt a different approach in which we use two

extensions of AdaBoost that were specifically intended for multiclass,

multi-label data. In the first extension, the goal of the learningalgorithm is to predict all and only all of the correct labels. Thus, the

learned classifier is evaluated in terms of its ability to predict a good

approximation of the set of labels associated with a given document.

In the second extension, the goal is to design a classifier that ranks

the labels so that the correct labels will receive the highest ranks. We

next describe BoosTexter, a system which embodies four versions of

boosting based on these extensions, and we discuss the

implementation issues that arise in multilabel text categorization.

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Title: A Comparison of Classifiers and Document

Representations for the Routing Problem

Author: H. Schutze, D.A. Hull,J.O. PedersenYear:1995

We compare two approaches to document routing,

relevance feedback via query expansion and statistical classification

with error minimization. We show that advanced classificationalgorithms perform 10-15% better than relevance feedback on the

Tipster document collection. Since learning algorithms based on

error minimization and numerical optimization are computationally

intensive and prone to over fitting in a high dimensional feature

space, it is necessary to apply some method of dimensionality

reduction. We examine two different approaches, latent semantic

indexing and feature selection of terms using a x2 -test of non

independence.

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Title: Content-Based Book Recommending Using Learning

for Text CategorizationAuthor: Raymond J. Mooney, Loriene Roy

Year: 1999

Recommender systems improve access to relevant products

and information by making personalized suggestions based on previous

examples of a user's likes and dislikes. Most existing recommender

systems use social filtering methods that base recommendations on otherusers' preferences. By contrast, content-based methods use information

about an item itself to make suggestions. This approach has the

advantage of being able to recommended previously unrated items to

users with unique interests and to provide explanations for its

recommendations. We describe a content-based book recommending

system that utilizes information extractionand a machine-learning

algorithm for text categorization. Initial experimental results demonstrate

that this approach can produce accurate recommendations. These

experiments are based on ratings from random samplings of items and

we discuss problems with previous experiments that employ skewed

samples of user-selected examples to evaluate performance.

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Title: Content-based Filtering in On-line Social NetworkAuthor: M. Vanetti, E. Binaghi, B. Carminati, M. Carullo and E. Ferrari

Year:2010.

This work is the first step of a wider project. The early

encouraging results we have obtained on the classification procedure

prompt us to continue with other work that will aim to improve the

quality of classification. Additionally, we plan to enhance our filtering

rule system, with a more sophisticated approach to manage those

messages caught just for the tolerance and to decide when a user shouldbe inserted into a BL. For instance, the system can automatically take a

decision about the messages blocked because of the tolerance, on the

basis of some statistical data (e.g., number of blocked messages from the

same author, number of times the creator has been inserted in the BL) as

well as data on creator profile (e.g., relationships with the wall owner,age, sex). Further, we plan to test the robustness of our system against

different adversary models. The development of a GUI to make easier

BL and filtering rule specification is also a direction we plan to

investigate.

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UniProfile improves on previous work by modeling policies as

first-class resources, protecting them in the same way as other

resources, providing _ne-grained control over policy disclosure,and clearly distinguishing between policy disclosure and policy

satisfaction, which gives users more flexibility in expressing their

authorization requirements. We also show that UniProfile can be

used with practical negotiation strategies without jeopardizing

autonomy in the choice of strategy, and present criteria under whichnegotiations using UniProfile are guaranteed to succeed in

establishing trust.

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Title: Achieving Secure, Scalable, and Fine-grained Data

Access Control in Cloud Computing

Author: S. Yu, C. Wang, K. Ren, and W. LouYear:2010

Cloud computing is an emerging computing paradigm in

which resources of the computing infrastructure are provided as

services over the Internet. As promising as it is, this paradigm also

brings forth many new challenges for data security and access control

when users outsource sensitive data for sharing on cloud servers, which

are not within the same trusted domain as data owners. To keep

sensitive user data confidential against untrusted servers, existing

solutions usually apply cryptographic methods by disclosing data

decryption keys only to authorized users. However, in doing so, thesesolutions inevitably introduce a heavy computation overhead on the

data owner for key distribution and data management when finegrained

data access control is desired, and thus do not scale well. The problem

of simultaneously achieving fine-grainedness, scalability, and data

confidentiality of access control actually still remains unresolved.

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Title: Inductive Learning Algorithms and Representations

for Text Categorization

Author: Susan Dumais, John Platt, David Heckerman, MehranSahami

Year: 1998

Here, we describe results from experiments using a

collection of hand-tagged financial newswire stories from Reuters.

We use supervised learning methods to build our classifiers, andevaluate the resulting models on new test cases. The focus of our

work has been on comparing the effectiveness of different inductive

learning algorithms (Find Similar, Nave Bayes, Bayesian Networks,

Decision Trees, and Support Vector Machines) in terms of learning

speed, real-time classification speed, and classification accuracy. We

also explored alternative document representations (words vs.

syntactic phrases, and binary vs. non-binary features), and training set

size.

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Title: Learning and Revising User Profiles: The

Identification of Interesting Web Sites

Author:M.J. Pazzani and D. BillsusYear: 1997

We discuss algorithms for learning and revising user profiles that can

determine which World Wide Web sites on a given topic would be

interesting to a user. We describe the use of a naive Bayesianclassifier for this task, and demonstrate that it can incrementally learn

profiles from user feedback on the interestingness of Web sites.

Furthermore, the Bayesian classifier may easily be extended to revise

user provided profiles. In an experimental evaluation we compare the

Bayesian classifier to computationally more intensive alternatives,

and show that it performs at least as well as these approaches

throughout a range of different domains. In addition, we empirically

analyze the effects of providing the classifier with background

knowledge in form of user defined profiles and examine the use of

lexical knowledge for feature selection. We find that both approaches

can substantially increase the prediction accuracy.

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Title: Towards the Next Generation of Recommender

Systems: A Survey of the State-of-the-Art and Possible

Extensions

Author:Gediminas Adomavicius and Alexander TuzhilinYear:2001

The paper presents an overview of the field of recommender

systems and describes the current generation of recommendation

methods that are usually classified into the following three main

categories: content-based, collaborative, and hybrid recommendation

approaches. The paper also describes various limitations of current

recommendation methods and discusses possible extensions that can

improve recommendation capabilities and make recommender

systems applicable to an even broader range of applications. These

extensions include, among others, improvement of understanding of

users and items, incorporation of the contextual information into the

recommendation process, support for multi-criteria ratings, and

provision of more flexible and less intrusive types of

recommendations.

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Title: Text Categorization with Support Vector Machines:

Learning with Many Relevant Features

Author:T. JoachimsYear: 1998

They are introduces support vector machines for text

categorization. It provides both theoretical and empirical evidence

that SVMs are very well suited for text categorization. The theoretical

analysis concludes that SVMs acknowledge the particular properties

of text In that experimental results show that SVMs consistently

achieve good performance on text on text categorization tasks,

outperforming existing methods substantially and significantly. With

their ability to generalized well in high dimensional feature spaces,

SVMs eliminate the need for feature selection, making the applicationof text categorization considerably easier. Another advantage of

SVMs over the conventional methods in their robustness.

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In this paper we present two constructions of Fuzzy IBE

schemes. Our constructions can be viewed as an Identity-Based

Encryption of a message under several attributes that compose a(fuzzy) identity. Our IBE schemes are both error-tolerant and secureagainst collusion attacks. Additionally, our basic construction does

not use random oracles. We prove the security of ourschemes underthe Selective-ID security model.

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Title: Hierarchical Attribute-Based Encryption for Fine-

Grained Access Control in Cloud Storage ServicesAuthor: G.Wang, Q. Liu, and J.Wu

Year:2010

Cloud computing, as an emerging computing paradigm,

enables users to remotely store their data into a cloud so as to enjoy

scalable services on-demand. Especially for small and medium-sized

enterprises with limited budgets, they can achieve cost savings andproductivity enhancements by using cloud-based services to manage

projects, to make collaborations, and the like. However, allowing

cloud service providers (CSPs), which are not in the same trusted

domains as enterprise users, to take care of confidential data, may

raise potential security and privacy issues. To keep the sensitive user

data confidential against untrusted CSPs, a natural way is to apply

cryptographic approaches, by disclosing decryption keys only to

authorized users.

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MODULES NAME

Authentication

Profile Generation

Accept Friends Request

Send Request

Share Photos

Post Comment or Text

Block Unwanted Comments

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MODULE DESCRIPTION

Authentication:

In this module User want to register the personal details inthe database and get the authentication processes to go forward. In

this module User want to give the database to admin all the

registration process is done by a admin. After the registration

process completed User can get the authentication permission, by

using username and password login website. If the user enters a

valid username/password combination they will be granted to access

data. If the user enter invalid username and password that user will

be considered as unauthorized user and denied access to that user.

Profile :

In this module user make our profile that details store indatabase the profile contains name, contact no, and email address,

photos, and other information. Logged users can see their details

and if they wish to change any of their information they can edit it.

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Send Request:

In this module user select friend to send request. logged user

view request accept our friend request.

Friends :

In this module user add new friends and view our friendsand details. Logged users can see their friend list and if they wish to

add friends

Post Photos:

In this module user get the photo from the database and add

new photo and publish on their wall.

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Block unwanted comments:

In this module we have to calculate the probability of the

message contents. That result will be display on the user private wall

with two options like accept and reject

Post comments:

In this module user post any photo in public wall, any one

post a comments for that photo. Unknown persons also postcomments but we dont know the character about the member in

case out of group.

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Authentication:

User Allow to Access Page

Database

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Profile:

UserCreate Profile Data Base

Accept Request:

UserNew Friends Data Base

View Friends

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Send Request:

UserSelect friend

Data Base

Send request

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Share Photos:

UserAdd New Photos

Data Base

View Photos

Publish

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Post comments:

Share Photo PostComments

Known or

Unknown

person

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Block the unwanted comments:

Post

CommentsCalculate Probability

based on content of

comments

Data Base

Display the

probability result on

user private wall

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GIVEN INPUT EXPECTED OUTPUTAuthentication (login /Registration)

Input: Register the user Details and give the username and

Password to login

Output: They will be granted to access the datas

Profile

Input: user enter our information like email id, contact no, and

other information

Output: create profile and store information into database

Friends

Input: user selects our friend to add

Output: add friend in our profile

Send Request

Input: user select friend to request

Output: Send request

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Post Photos

Input: User post some photos on their wall.

Output: Display the photos.

Post Comments

Input: Known or unknown persons are send a comment.

Output: The comments will display.

Block the unwanted comments

Input: The comments compare to the database data and calculate the

probability.

Output: The probability results display on user private wall so wehave to control directly.

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TECHNIQUE USEDMachine Learning (ML) Text Categorization Techniques:

Step 1:SHORT TEXT CLASSIFIER

i)Text Representation

we consider three types of features, BoW, Document properties (Dp)

andContextual Features (CF). they are entirely derived from the

information contained within the text of the message. Text

representation using endogenous knowledge has a good general

applicability; however, in operational settings, it is legitimate to use

also exogenous knowledge, i.e., any source of information outside themessage body but directly or indirectly related to the message itself.

ii) Machine Learning-Based Classification

We address short text categorization as a hierarchical twolevel

classification process. The first-level classifier performs a binary hard

categorization that labels messages as Neutral and Nonneutral. Thefirst-level filtering task facilitates thesubsequent second-level task in

which a finer-grained classification is performed. The second-level

classifier performs a soft-partition of Nonneutral messages assigning

a given message a gradual membership to each of the nonneutral

classes.

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iii)Radial Basis Function Networks (RBFN)

RFBNs have a single hidden layer of processing units with local,

restricted activation domain: a Gaussian function is commonly used,

but any other locally tunable function can be used. They were

introduced as a neural network evolution of exact interpolation, andare demonstrated to have the universal approximation property.

Step 2: FILTERING RULES AND BLACKLIST

MANAGEMENT

i)Filtering Rules:

a) Creator specification

This implies to state conditions on type, depth, and trust values of the

relationship( s) creators should be involved in order to apply them the

specified rules. A creator specification creatorSpec implicitly denotes

a set of OSN users

A set of attribute constraints of the form an OP av, where an is a user

profile attribute name,av and OP are, respectively, a profile attributevalue and a comparison operator, compatible with an sdomain.

A set of relationship constraints of the form (m, rt, minDepth,

maxTrust) denoting all the OSN users participating with user m in a

relationship of type rt, having a depth greater than or equal to

minDepth, and a trust value less than or equal to maxTrust.

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b)Filtering Rule

author is the user who specifies the rule;

creatorSpec is a creator specification, specified according to

Definition 1;

contentSpec is a Boolean expression defined on content constraints ofthe form (C, ml) where C is a class of the first or second level and ml

is the minimum membership level threshold required for class C to

make the constraint satisfied;

action { fblock notify} denotes the action to be performed by the

system on the messages matching contentSpec and created by usersidentified by creatorSpec.

ii) Blacklists

A BL rule is a tuple (author, creatorSpec, creatorBehavior, T), where

author is the OSN user who specifies the rule, i.e., the wall owner;

creatorSpec is a creator specification, specified according toDefinition 1;

creatorBehavior consists of two components RFBlocked and

minBanned.

T denotes the time period the users identified by creatorSpec and

creatorBehavior have to be banned from author wall.

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SYSTEM SPECIFICATION

SOFTWARE REQUIREMENTS:

Operating system : Windows7

IDE : Microsoft Visual Studio .NET 2010

Front End : WPF

Coding Language : C#

Backend : SQL Server 2008

HARDWARE REQUIREMENTS:

Processor : Pentium Dual Core 2.00GHZ

Hard disk : 40 GB Mouse : Logitech.

RAM : 2GB(minimum)

Keyboard : 110 keys enhanced.

SYSTEM DESIGN

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SYSTEM DESIGNIn our project user make our profile that details store in database.

Logged users can see their details and if they wish to change any of

their information they can edit it. User add new friends and view our

friends and details. Logged users can see their friend list and if they

wish to add friends user add new friends and view our friends and

details. Logged users can see their friend list and if they wish to add

friends user post new message and view our friend message. Logged

users post a photo on their wall at the time known or unknown

persons are post comments about that photo. Sometimes they are

sending unwanted messages like vulgar, politics, Violence etc. So wehave to calculate the probability based on the comment contents. And

those results will send to user private wall. Based on the result user

will take the decision.

USE CASE DIAGRAM:

A use case diagram is a type of behavioral diagram created froma Use-case analysis. The purpose of use case is to present overview of

the functionality provided by the system in terms of actors, their goals

and any dependencies between those use cases.

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OSN User

Known Person

Unknown Person

Register/Login

Profile

Accept the Request

Send request

Post Photos and display

Comments

Calculate the Probability

OSN Managers

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CLASS DIAGRAM

A class diagram in the UML is a type of static structure diagram

that describes the structure of a system by showing the systems

classes, their attributes, and the relationships between the classes.

Private visibility hides information from anything outside the

class partition. Public visibility allows all other classes to view the

marked information.

Protected visibility allows child classes to access information

they inherited from a parent class.

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OBJECT DIAGRAM:

An object diagramin the Unified Modeling Language (UML)

is a diagram that shows a complete or partial view of the structure ofa modeled system at a specific time.

An Object diagram focuses on some particular set of object

instances and attributes, and the links between the instances. A

correlated set of object diagrams provides insight into how an

arbitrary view of a system is expected to evolve over time.Object diagrams are more concrete than class diagrams, and

are often used to provide examples, or act as test cases for the class

diagrams. Only those aspects of a model that are of current interest

need be shown on an object diagram.

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Photos

Add photos=****

Message

Post message=****

Probability=****

User Login

Name=*****

Password=******

Profile

Category=*****

Property=*****

Friends

Characters=****

Book Informations=****

Photos

Add photos=****

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STATE DIAGRAMA state diagram is a type of diagram used in computer

science and related fields to describe the behavior of systems. State

diagrams require that the system described is composed of a finite

number of states; sometimes, this is indeed the case, while at other

times this is a reasonable abstraction. There are many forms of state

diagrams, which differ slightly and have different semantics.

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UserKnown/Unknown

Person

Send

Request

Accept the

Request

Share the

Photo

Post

Comments

Block the unwanted

comments

Login

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ACTIVITY DIAGRAM:

Activity diagram are a loosely defined diagram to show

workflows of stepwise activities and actions, with support forchoice, iteration and concurrency. UML, activity diagrams can be

used to describe the business and operational step-by-step

workflows of components in a system. UML activity diagrams

could potentially model the internal logic of a complex operation.

In many ways UML activity diagrams are the object-orientedequivalent of flow charts and data flow diagrams (DFDs) from

structural development.

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Login

User Known/Unknown

Persons

Send

Request

Accept the

Requests

Share

Photos/Messages

Post the

comments

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SEQUENCE DIAGRAM:

A sequence diagram in UML is a kind of interaction diagram

that shows how the processes operate with one another and in whatorder.

It is a construct of a message sequence chart. Sequence

diagrams are sometimes called Event-trace diagrams, event

scenarios, and timing diagrams.

The below diagram shows the sequence flow shows how the

process occurs in this project.

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Login User Known/Unknow

n Person

FirewallPublic Wall

Login

Login

Send request

Acccept the requests

Share Photos

Post Comments

Classification based on the content

Calculate the Probability based on the comments content

Direct Control Accept/Reject

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COLLABORATION DIAGRAM:

A collaboration diagram show the objects and relationships

involved in an interaction, and the sequence of messagesexchanged among the objects during the interaction.

The collaboration diagram can be a decomposition of a class,

class diagram, or part of a class diagram. It can be the

decomposition of a use case, use case diagram, or part of a usecase diagram.

The collaboration diagram shows messages being sent

between classes and object (instances). A diagram is created for

each system operation that relates to the current development cycle

(iteration).

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UserKnown/Unkno

wn Person

Login

FirewallPublic

Wall

2: Login

6: Post Comments

7: Classification based on the content

8: Calculate the Probability based on the comments content

1: Login

3: Send request

4: Acccept the requests

5: Share Photos

9: Direct Control Accept/Reject

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COMPONENT DIAGRAM:

The component diagram's main purpose is to show the structural

relationships between the components of a system. A component

represented implementation items, such as files and executables.

Unfortunately, this conflicted with the more common use of the term

component," which refers to things such as COM components. Over

time and across successive releases of UML, the original UML

meaning of components was mostly lost. UML 2 officially changesthe essential meaning of the component concept; in UML 2,

components are considered autonomous, encapsulated units within a

system or subsystem that provide one or more interfaces.

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Calculate

Probability

User Known/Unknown

Person

FireWall

Classifier

Login

Black

List

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DATA FLOW DIAGRAM:

A Data flow diagram (DFD) is a graphical representation of

the flowof data through an information system. It differs fromthe flowchart as it shows the data flow instead of the control flow

of the program. A data flow diagram can also be used for the

visualization of data processing. The DFD is designed to show

how a system is divided into smaller portions and to highlight the

flow of data between those parts.

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LEVEL 0

LEVEL 1

LEVEL 2

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LEVEL 3

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LEVEL 4

All Levels:

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In this data flow diagram (DFD), trusted authority is the

head for this project. Domain authority is the subhead for this

project. It performs the administrator operation. Data Owner first get

the permission from the domain authority and encrypt the data andstore the data in cloud storage. At a time trusted authority create one

decryption key to relevant data and provide the decryption key to

the domain authority. Data Consumer pay some amount of money to

the domain authority and get the decryption key. Finally get and

decrypt the data from cloud storage.

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E-R DIAGRAM:

In software engineering, an entity-relationship

model (ERM) is an abstract and conceptual representation of data.Entity-relationship modeling is a database modeling method, used

to produce a type of conceptual schema or semantic data model of a

system, often a relational database, and its requirements in a top-

down fashion. Diagrams created by this process are called entity-

relationship diagrams, ER diagrams, or ERDs.An entity-relationship (ER) diagram is a specialized graphic that

illustrates the relationships between entities in a database. ER

diagrams often use symbols to represent three different types of

information. Boxes are commonly used to represent entities.

Diamonds are normally used to represent relationships and ovals

are used to represent attributes.

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Users Fire Wall

Login

Accept theothers request

Known/Unknown

Persons

CalculateProbability

ClassifiersBlack List

LoginRegister Post

Comments

Share

Photos

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SYSTEM ARCHITECTURE

The OSN have three layers, there are graphical user

interface, social network application and social network managers.

The social network managers handle the basic functionalities like

profile management, network based function etc. Now a daysthe

online social network users receive some unwanted comments But

in this project focused on other two layers and apply some new

condition. Application layer have short text classifier and content

based message filtering. Short text classifier classifying themessages based on the content. Content based message filter have

black list and filtering policies. First, find relationship between the

user and message senders and it will filter and calculate the

probabilities using classifier. And the send a empty message below

the probabilities result to the user. So our proposed system willgive the direct control to the user that what kind of messages

displays on their wall.

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Future Enhancement Module Diagram & Description

Future Enhancement Description:

We plan to extend this work in several directions. In

proposed system we have black Comments temporarily based on

the contents and create specification. But, in our future we have to

block the permanently based on the content probability and creator

specifications.

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Permanent Block:

Creator

Specification

User

Data base

Known/Unknown

Persons

Black

Permanently

Share Photo

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GIVEN INPUT EXPECTED OUTPUT

Permanently Block:

Input: The unwanted message sender details stored in database.Output: that message sender based on the relationship and content

block permanently.

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ADVANTAGES

Proposed system give the control to what kind of messages post

on own wall.

It is secure because filter wall act as Administrator.

The core components of the proposed system are the Content-Based Messages Filtering (CBMF) and the Short Text Classifier

modules.

Rule layer adopted for filtering unwanted messages. We start by

describing FRs, then we illustrate the use of BLs.

BL mechanism to avoid messages from undesired creators,

independent from their contents.

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APPLICATION

Online Social Network Application:

In online social networks application every account holder post the

comments for the user sharing files like messages or photos. In this

process some known or unknown persons post unwanted comments

like politics, vulgar, violence and etc. So users have direct control

which kind of messages post on their wall. In this concept we have to

implement all kind of online social networks like facebook, twitter,

orkut etc.

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CONCLUSION

We have presented system direct control to the user block

unwanted messages on their social network wall. The system using

the machine language soft classifier to label the contents are

Neutral and Nonneutral. And then applying the Filter Rule based on

the creators. Moreover, the flexibility of the system in terms of

filtering options is enhanced through the management of BLs.

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THANK YOU