Copy Of Review-1 Report(3)2

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS A.V.C COLLEGE OF ENGINEERING – MANNAMPANDAL -609305 MAYILADUTHURAI DEPARTMENT OF COMPUTER APPLICATIONS

PROJECT TITLE PRIVACY PRESERVING DATA MINING SYSTEMS

FIRST PROJECT REVIEW REPORT Name: D.S.Arun Francis

Reg.No: 80106621006

Course:

Batch : 2006-2009

MCA

Date : 23-3-2009 External Guide Name :Mr.K.Vijayan (Company)

Internal Guide Name: Mr.T.RamKumar (College)

COMPANY NAME AND ADRRESS:

15,T.B.Road,Mappalayam, Madurai-625 010.

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS INTRODUCTION Many organizations collect large amounts of data about their clients and customers. Such data was used for record keeping. Data mining can extract valuable knowledge from this data .Organizations can obtain better results by pooling their data together. However, the collected data may contain sensitive or private information about the organizations or their customers, and privacy concerns are exacerbated if data is shared between multiple organizations. Distributed data mining is concerned with the computation of models from data that is distributed among multiple participants. Privacy-preserving distributed data mining seeks to allow for the cooperative computation of such models without the cooperating parties revealing any of their individual data items. Our project makes two contributions in privacy-preserving data mining. First, we introduce the concept of arbitrarily partitioned data, which is a generalization of both horizontally and vertically partitioned data. Second, we provide an efficient privacy-preserving protocol for k-means clustering in the setting of arbitrarily partitioned data. Privacy-preserving distributed data mining allows the cooperative computation of data mining algorithms without requiring the participating organizations to re- veal their individual data items to each other. We present a simple I/O-efficient k-clustering algorithm that was designed with the goal of enabling a privacy-preserving version of the algorithm. Our experiments show that this algorithm produces cluster centers that are, on average, more accurate than the ones produced by the well known iterative k-means algorithm. We use our new algorithm as the basis for a communicationefficient privacy-preserving k-clustering protocol for databases that are horizontally partitioned between two parties. Unlike existing privacy-preserving protocols based on the k-means algorithm, this protocol does not reveal intermediate candidate cluster centers. In this work, we propose methods for constructing the dissimilarity matrix of objects.

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS Existing System: The earlier techniques are data perturbation techniques for privacy preserving classification model construction on centralized data , techniques for privacy preserving association rule mining in distributed environments . Techniques from secure multiparty computation form one approach to privacy-preserving data mining. Yao’s general protocol for secure circuit evaluation can be used to solve any two-party privacy-preserving distributed data mining problem. However, since data mining usually involves millions or billions of data items, the communication cost of this protocol renders it impractical for these purposes. Proposed System: In this project, we propose a privacy preserving clustering technique on horizontally partitioned data. Our method is based on constructing the dissimilarity matrix of objects from different sites in a privacy preserving manner which can be used for privacy preserving clustering, as well as database joins, record linkage and other operations that require pair-wise comparison of individual private data objects distributed over multiple sites. We handle both numeric and alphanumeric valued attributes. Our main contributions can be summarized as follows: • Introduction of a protocol for secure multi-party computation of a dissimilarity matrix over horizontally partitioned data, • Proof of security of the protocol, analysis of communication costs , • Secure comparison methods for numeric, alphanumeric and categorical attributes, • Application to privacy preserving clustering.

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS

Future Enhancement: Most of the previous studies investigated the problem and proposed solutions based on the assumption that all parties are honest or semi-honest. While it is sometimes useful, this assumption substantially underestimates the capability of adversaries and thus does not always hold in practical situations. We considered a space of more powerful adversaries which include not only honest and semi-honest adversaries but also those who are weakly malicious and strongly malicious. Many extensions to our work exist, including 1) extending the information sharing function from intersection to other operations, and 2) dealing with multiple parties in the system, including dealing with correlated attacks from multiple adversaries.

DATA FLOW DIAGRAM

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS

Cluster Checking Start FTP Server

System-1 Decrypt Receive Encrypt

IF EXIT

Choose File

System-2

Send Receive

Choose File

USECASE DIAGRAM

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS

System1 File Choosing

Starting the FTP server

Clustering the Selected File

Encryption

Decryption

Reclustering the clustered File

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS MODULE DESCRIPTIONS Modules: 1. Data collection and mining

2. Formation of Cluster 3. Privacy preservation 4. Comparison evaluation of algorithms 1. Data Collection and mining: The data collection is the important part of the project. The dataset is prepared by Integrating the organizations details. Such data was initially used only for record keeping. These large collections of data could be “mined” for knowledge that could improve the performance of the organization. While much data mining occurs on data within an organization, it is quite common to use data from multiple sources in order to yield more precise or useful knowledge. However, privacy and secrecy considerations can prohibit organizations from being willing or able to share their data with each other. Therefore we preserve Privacy-preserving data mining technique. 2. Formation of Cluster: The k-means of clustering algorithm is used for cluster formation. K-Means Clustering and Fuzzy Clustering are different than Hierarchical Clustering and Diversity Selection in that the number of clusters, K, needs to be determined at the onset. The goal is to divide the objects into K clusters such that some metric relative to the centroids of the clusters is minimized. If the clustering is over binary objects, medoids need to be used instead of centroids. A medoid is just a bit string that minimizes the sum of distances to all objects in the cluster. Various metrics to the centroids that can be minimized include •

Maximum distance to its centroid for any object.

•

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS Sum of the average distance to the centroids over all clusters.

•

Sum of the variance over all clusters.

•

Total distance between all objects and their centroids.

•

3. Privacy preservation: Privacy-preserving data mining solutions have been presented both with respect to

horizontally and vertically partitioned databases, in which either different data objects with the same attributes, are owned by each party, or different attributes for the same data objects are owned by each party, respectively. We introduce the notion of arbitrarily partitioned data, which generalizes both horizontally and vertically partitioned data. In arbitrarily partitioned data, different attributes for different items can be owned by either party. Although extremely “patchworked” data is unlikely in practice, one advantage of considering arbitrarily partitioned data is that protocols in this model apply both to horizontally and vertically partitioned data, as well as to hybrid that are mostly, but not completely, vertically or horizontally partitioned. 4. Comparison evaluation of algorithms : In this project , we present a simple deterministic algorithm, Recluster, for I/O-efficient k-clustering. This algorithm, which was explicitly designed with conversion to a privacy-preserving version in mind, examines each data item only once and uses only sequential access to the data. For fixed k, Recluster runs in O(n) time and uses O(log n) space. Our experimental results show that Recluster is, on average, more accurate in identifying cluster centers than the k-means clustering algorithm. Although there are other clustering algorithms that improve on the k-means algorithm, this is the first for which an efficient cryptographic privacypreserving version has been demonstrated.

SCREEN SHOTS

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS

DATABASE DESCRIPTION PRIVACY PRESERVATION :

FIELD

TYPE

ID

Integer

IP ADDRESS

String

LOGIN 1: FIELD

TYPE

ID

Integer

USERNAME

String

PASSWORD

String

LOGIN 2: FIELD

TYPE

ID

Integer

USERNAME

String

PASSWORD

String

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS SAMPLE CODING

/*---------------Centroid.java-----------------*/ package distance; class Centroid { private double mCx, mCy; private Cluster mCluster; public Centroid(double cx, double cy) { this.mCx = cx; this.mCy = cy; } public void calcCentroid() { //only called by CAInstance int numDP = mCluster.getNumDataPoints(); double tempX = 0, tempY = 0; int i; //caluclating the new Centroid for (i = 0; i < numDP; i++) { tempX = tempX + mCluster.getDataPoint(i).getX(); //total for x tempY = tempY + mCluster.getDataPoint(i).getY(); //total for y } this.mCx = tempX / numDP; this.mCy = tempY / numDP; //calculating the new Euclidean Distance for each Data Point tempX = 0; tempY = 0; for (i = 0; i < numDP; i++) { mCluster.getDataPoint(i).calcEuclideanDistance(); } //calculate the new Sum of Squares for the Cluster mCluster.calcSumOfSquares(); } public void setCluster(Cluster c) { this.mCluster = c; } public double getCx() {

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS return mCx;

} public double getCy() { return mCy; } public Cluster getCluster() { return mCluster; }

} /*-----------------Cluster.java----------------*/ package distance; import java.util.Vector; class Cluster { private String mName; private Centroid mCentroid; private double mSumSqr; private Vector mDataPoints; public Cluster(String name) { this.mName = name; this.mCentroid = null; //will be set by calling //setCentroid() mDataPoints = new Vector(); } public void setCentroid(Centroid c) { mCentroid = c; } public Centroid getCentroid() { return mCentroid; } public void addDataPoint(DataPoint dp) { //called from CAInstance dp.setCluster(this); //initiates a inner call to //calcEuclideanDistance() in DP. this.mDataPoints.addElement(dp); calcSumOfSquares(); } public void removeDataPoint(DataPoint dp) {

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS this.mDataPoints.removeElement(dp); calcSumOfSquares(); } public int getNumDataPoints() { return this.mDataPoints.size(); } public DataPoint getDataPoint(int pos) {

return (DataPoint) this.mDataPoints.elementAt(pos); } public void calcSumOfSquares() { //called from Centroid int size = this.mDataPoints.size(); double temp = 0; for (int i = 0; i < size; i++) { temp = temp + ((DataPoint) this.mDataPoints.elementAt(i)).getCurrentEuDt(); } this.mSumSqr = temp; } public double getSumSqr() { return this.mSumSqr; } public String getName() { return this.mName; } public Vector getDataPoints() { return this.mDataPoints; }

} // GRAPH.java// package Graph; import java.awt.Point; public class Cluster{ public Point point=null;

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS public int finished=0; public Cluster(int x,int y) { // TODO Auto-generated constructor stub x=setX(x); y=setY(y); this.point=new Point(x,y); } public int setX(int i){ i=(int) (Graph.p2.getX()+(i*(Graph.Xlength/Graph.Xedge))); //i=(int) (Graph.p2.getX()+(i*Graph.Xp)); return i; } public int setY(int j){ j=(int)(Graph.p2.getY()-(j*(Graph.Ylength/Graph.Yedge))); //j=(int) (Graph.p2.getY()-(j*Graph.Yp)); return j; } } /*----------------DataPoint.java----------------*/ package distance; public class DataPoint { private double mX,mY; private String mObjName; private Cluster mCluster; private double mEuDt; public DataPoint(double x, double y, String name) { this.mX = x; this.mY = y; this.mObjName = name; this.mCluster = null; } public void setCluster(Cluster cluster) { this.mCluster = cluster; calcEuclideanDistance(); } public void calcEuclideanDistance() { mEuDt = Math.sqrt(Math.pow((mX - mCluster.getCentroid().getCx()), 2) + Math.pow((mY - mCluster.getCentroid().getCy()), 2)); // System.out.println(" medu "+mEuDt); } public double testEuclideanDistance(Centroid c) {

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS return Math.sqrt(Math.pow((mX - c.getCx()), 2) + Math.pow((mY c.getCy()), 2)); } public double getX() { return mX; } public double getY() { return mY; } public Cluster getCluster() { return mCluster; } public double getCurrentEuDt() { return mEuDt; } public String getObjName() { return mObjName; } } // GRAPH// package Graph; import java.awt.Color; import java.awt.Container; import java.awt.Graphics; import java.awt.Point; import java.awt.Polygon; import java.util.Vector; import javax.swing.JFrame; import javax.swing.JPanel; import javax.swing.UIManager; import javax.swing.*; import Ran.Ran; import Util.Util; public class Graph extends JFrame implements Runnable { public JFrame frame=null; public Container container=null; public static JPanel panel1=null; public String GraphName=""; public Point p1=new Point(50, 0); public static Point p2=new Point(50, 600); public Point p3=new Point(900, 600); public Polygon polygon=new Polygon();

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS public static boolean b=true; public static int Xlength=0; public static int Ylength=0; public String Xname=""; public String Yname=""; public char[] Ychar=null; public static int Xedge=0; public static int Yedge=0; public int Xunit=0; public int Yunit=0; public boolean UnitDisplay=true; public int Xn=0; public static int Xp=0; public int Yn=0; public static int Yp=0; public Vector graph=new Vector(); public int OvalW=30; public int OvalH=30; public Graph(String string) { this.GraphName=string; frame=new JFrame(string); container=frame.getContentPane(); Xlength=(int) (p3.getX()-p2.getX()); Ylength=(int) (p2.getY()-p1.getY()); panel1=new JPanel(){ public void paint(Graphics g){ drawGraph(g); } }; polygon.addPoint(50,0); polygon.addPoint(900,0); polygon.addPoint(900,600); polygon.addPoint(50,600); frame.setBounds(0, 0, 950, 650); container.setBounds(0, 0, 900, 750); panel1.setBounds(0, 0, 900, 750); container.add(panel1); frame.setLocationRelativeTo(null); frame.setVisible(true); } public void setXname(String string){

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS this.Xname=string;

} public void setYname(String string){ this.Yname=string; this.Ychar=this.Yname.toCharArray(); } public void setXedge(int i){ this.Xedge=i; } public void setYedge(int i){ this.Yedge=i; } public void setXunit(int i){ this.Xunit=i; } public void setYunit(int i){ this.Yunit=i; } public void setUnitDisplay(boolean b){ this.UnitDisplay=b; repaint(); } public void Add(int x,int y){ graph.add(new Cluster(x,y));} public void Dopaint(){ repaint();} public void drawGraph(Graphics g){ g.clearRect(0, 0, panel1.getWidth(), panel1.getHeight()); g.setColor(Color.black); try { Xn=Xedge/Xunit; Xp=Xlength/Xn; Yn=Yedge/Yunit; Yp=Ylength/Yn; } catch (RuntimeException e) { } System.out.println("System running"); g.drawString("0", (int)(p2.getX()-10), (int)(p2.getY()+10)); g.drawLine((int)p1.getX(),(int)p1.getY(),(int)p2.getX(),(int)p2.get g.drawLine((int)p2.getX(),(int)p2.getY(),(int)p3.getX(),(int)p3.get g.drawString(Xname,(int) p2.getX()+200,(int)p2.getY()+20); g.drawString("RE-CLUSTER", 400, 700); for (int i = 0,j=200; i < Ychar.length; i++) {

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS j=j+30; g.drawString(""+Ychar[i],(int)p1.getX()-20,(int)p1.getY()+ } try { if (UnitDisplay) { for (int i = 1; i <= Xn; i++) { g.drawString(""+(i*Xunit), (int) (p2.getX()+(i*Xp)), (int) (p2.getY() + 10)); } for (int i = 1; i <= Yn; i++) { g.drawString(""+(i*Yunit), (int) (p2.getX()-10), (int) (p2.getY (i*Yp))); }} catch (RuntimeException e) { } for (int j=0,i = 0; j < graph.size(); j++,i++) { System.out.println("Before oval"); int x1=(int)graph.elementAt(j).point.getX(); int y1=(int)(graph.elementAt(j).point.getY()); int x2=(int)graph.elementAt(j).point.getX(); int y2=(int)(graph.elementAt(j).point.getY()); g.setColor(Color.RED); System.out.println("sys"+x1+" "+y1+" "+x2+" "+y2); g.fillOval(x1-3, y1-3, 6, 6); g.drawOval( (int) (graph.elementAt(j).point.getX() - (OvalW / 2)), (int) (graph.elementAt(j).point.getY() - (OvalH / 2)), OvalW, OvalH); g.setColor(Color.BLACK); (int)graph.elementAt(j).point.getY(), 10, 20); } } System.out.println("Before for1"); for (int i = 0,j=0;j<10000 ; i++) { Point tPoint=new Point(Ran.getRan(),Ran.getRan()); if(polygon.contains(tPoint)){ g.drawOval((int)tPoint.getX(), (int)tPoint.getY(), 1, j++; } } } public static void main(String[] args) { try{ UIManager.setLookAndFeel("com.jtattoo.plaf.hifi.HiFiLookAndFeel }catch(Exception e){ e.printStackTrace(); }

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS Graph graph=new Graph("PRIVACY PRESERVING DATABASE APPLICATION TESTING graph.setXname("Xname"); graph.setYname("Yname"); graph.setXedge(100); graph.setYedge(100); graph.setXunit(10); graph.setYunit(10); graph.Add(10,10); graph.Add(20,10); graph.Add(30,10); graph.Add(40,10); graph.Add(50,10); graph.Add(10,20); graph.Add(20,20); graph.Add(30,20); graph.Add(40,20); graph.Add(50,20); graph.Add(10,30); graph.Add(20,30); graph.Add(30,30); graph.Add(40,30); graph.Add(50,30); graph.Add(10,40); graph.Add(20,40); graph.Add(30,40); graph.Add(40,40); graph.Add(50,40); graph.Add(10,50); graph.Add(20,50); graph.Add(30,50); graph.Add(40,50); graph.Add(50,50); for (int i = 10; i < 100; i=i+10) { graph.setUnitDisplay(true); } public void run() { while(true){ repaint(); this.panel1.repaint(); } } REPORTS DETAILS

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS

 After browsing the ARFF file, click submit button .If the corresponding arff file is present

then a success page report will open indicating the ARFF file.  Give the IP address in the textbox and then it get connected to the FTP server.(192.168.1.60)

 After the success page of ARFF file is Opened the report of clustering objects will be shown in specifically in the graph.

 In the Login Page give the corresponding username and login,then the ARFF file will be opened.  Next Stage the ARFF file is encrypted in the SYSTEM1 and is viewed.  The Encrypted file are Decrypted and is visualized in the SYSTEM2.  After the process of encryption and decryption the process of reclustering takes place and Clustered file is being reclustered..

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS TESTING DETAILS

SYSTEM TESTING: Testing is performed to identify errors. It is used for quality assurance. Testing is an integral part of the entire development and maintenance process. The goal of the testing during phase is to verify that the specification has been accurately and completely incorporated into the design, as well as to ensure the correctness of the design itself. For example the design must not have any logic faults in the design is detected before coding commences, otherwise the cost of fixing the faults will be considerably higher as reflected. Detection of design faults can be achieved by means of inspection as well as walkthrough. Testing is one of the important steps in the software development phase. Testing checks for the errors, as a whole of the project testing involves the following test cases:  Static analysis is used to investigate the structural properties of the Source code.  Dynamic testing is used to investigate the behavior of the source code by executing the program on the test data. UNIT TESTING: Unit testing is conducted to verify the functional performance of each modular component of the software. Unit testing focuses on the smallest unit of the software design (i.e.), the module. The white-box testing techniques were heavily employed for unit testing. FUNCTIONAL TESTS: Functional test cases involved exercising the code with nominal input values for which the expected results are known, as well as boundary values and special values, such as logically related inputs, files of identical elements, and empty files.

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS Three types of tests in Functional test:  Performance Test  Stress Test  Structure Test PERFORMANCE TEST: It determines the amount of execution time spent in various parts of the unit, program throughput, and response time and device utilization by the program unit. STRESS TEST: Stress Test is those test designed to intentionally break the unit. A Great deal can be learned about the strength and limitations of a program by examining the manner in which a programmer in which a program unit breaks. STRUCTURED TEST: Structure Tests are concerned with exercising the internal logic of a program and traversing particular execution paths. The way in which White-Box test strategy was employed to ensure that the test cases could Guarantee that all independent paths within a module have been have been exercised at least once.  Exercise all logical decisions on their true or false sides.  Execute all loops at their boundaries and within their operational bounds.  Exercise internal data structures to assure their validity.  Checking attributes for their correctness.  Handling end of file condition, I/O errors, buffer problems and textual errors in output information INTEGRATION TESTING: Integration testing is a systematic technique for construction the program structure while at

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS the same time conducting tests to uncover errors associated with interfacing. i.e., integration testing is the complete testing of the set of modules which makes up the product. The objective is to take untested modules and build a program structure tester should identify critical modules. Critical modules should be tested as early as possible. One approach is to wait until all the units have passed testing, and then combine them and then tested. This approach is evolved from unstructured testing of small programs. Another strategy is to construct the product in increments of tested units. A small set of modules are integrated together and tested, to which another module is added and tested in combination. And so on. The advantages of this approach are that, interface dispenses can be easily found and corrected. The major error that was faced during the project is linking error. When all the modules are combined the link is not set properly with all support files. Then we checked out for interconnection and the links. Errors are localized to the new module and its intercommunications. The product development can be staged, and modules integrated in as they complete unit testing. Testing is completed when the last module is integrated and tested. VALIDATION TESTING: Software validation is achieved through a series of tests that demonstrates conformity with requirements. A test plan outlines the classes of test to be conducted and a test procedure defines specific test cases that will be used to demonstrate conformity with requirements. Thus the proposed system under consideration has been tested by validation and found to be working satisfactorily. PROGRAM TESTING: The logical and syntax errors have been pointed out by program testing. A syntax error is an error in a program statement that in violates one or more rules of the language in which it is written. An improperly defined field dimension or omitted keywords are common syntax error. These errors are shown through error messages generated by the computer. A logic error on the other hand deals with the incorrect data fields, out-off-range items and invalid combinations. Since the compiler s will not deduct logical error, the programmer must examine the output. Condition

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS testing exercises the logical conditions contained in a module. The possible types of elements in a condition include a Boolean operator, Boolean variable, a pair of Boolean parentheses A relational operator or on arithmetic expression. Condition testing method focuses on testing each condition in the program the purpose of condition test is to deduct not only errors in the condition of a program but also other a errors in the program. SECURITY TESTING: Security testing attempts to verify the protection mechanisms built in to a system well, in fact, protect it from improper penetration. The system security must be tested for invulnerability from frontal attack must also be tested for invulnerability from rear attack. During security, the tester places the role of individual who desires to penetrate system. SOFTWARE TESTING STRATEGIES: A software testing strategy provides a road map for the software developer. Testing is a set activity that can be planned in advance and conducted systematically. For this reason a template for software testing a set of steps into which we can place specific test case design methods should be strategy should have the following characteristics:  Testing begins at the module level and works “outward” toward the integration of the entire computer based system.  Different testing techniques are appropriate at different points in time.  The developer of the software and an independent test group conducts testing.  Testing and Debugging are different activities but debugging must be accommodated in any testing strategy.

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS

INDIVIDUAL PROJECT SCHEDULE

S.No

Activity Description

Planned Date

Actual Date

Status

Remark

CLIENT 1 : 1.1

File choosing

28-12-08

3-1-09

Finished

No

1.2

Using clustering algorithm

4-1-08

18-1-09

Finished

No

1.3

Viewing the clustered graph

24-1-09

3-2-09

Finished

No

1.4

Login form

5-2-09

7-2-09

Finished

No

1.5

Start the FTP server

10-2-09

19-2-09

Finished

No

No

CLIENT 2 : 2.1

Login Form

20-2-09

22-2-09

Finished

2.2

Corresponding IP address

24-2-09

27-2-09

Finished

2.3

File sending option(client1)

2-3-09

8-3-09

Finished

2.4

Encryption process(client2)

2.5

Decryption process(client1)

2.6

File receiving option(client1)

D.S.Arun Francis Signature of the Student

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS

STUDENT PERFORMANCE REPORT Comments on the Project by External Guide : He has given his full effort to the project. He has learned a sound knowledge in java swing and in JSP. • He finished his assigned task in the modules. • He took part in all the project activities and work in our team. • •

Sign of External Guide Comments on the Project by Internal Guide :

Sign of Internal Guide

Sign of Director

A.V.C.COLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS