This document describes a proposed system for privacy-preserving personalized web search. It discusses hardware and software specifications, existing systems that have privacy and personalization issues, and the proposed system which formulates the problem as risk profile generalization. The proposed system includes two algorithms, GreedyDP and GreedyIL, to support runtime profiling while maximizing privacy protections. It also describes modules for the system, including image loading, adjustment tools, filtering, pruning, resizing, and system flow.

1. Supporting Privacy Protection in Personalized Web Search
NAME:
ENROLL NO:

2. INTRODUCTION
 The web search engine has long become the most important portal for
ordinary people looking for useful information on the web.
 However, users might experience failure when search engines return
irrelevant results that do not meet their real intentions
 As the expense, user information has to be collected and analyzed to figure
out the user intention behind the issued query.

3. HARDWARE AND SOFTWARE USED:
HARDWARE SPECIFICATION:
• PROCESSOR : i8
• RAM : 4GB
• HARDWARE : 1TB

4. SOFTWARE SPECIFICATION
• PLATFORM : JAVA 1.5
• OPERATING SYSTEM : WINDOWS 8

5. EXISTING SYSTEM:
• The profile-based personalization may not even help to improve the search quality for some ad hoc queries, though exposing user
profile to a server has put the user’s privacy at risk.
• The existing methods do not take into account the customization of privacy requirements. This probably makes some user privacy to
be overprotected while others insufficiently protected.
• Many personalization techniques require iterative user interactions when creating personalized search results. They usually refine the
search results with some metrics which require multiple user interactions, such as rank scoring, average rank, and so on.
• It is infeasible for runtime profiling, as it will not only pose too much risk of privacy breach, but also demand prohibitive processing
time for profiling. Thus, we need predictive metrics to measure the search quality and breach risk after personalization, without
incurring iterative user interaction.
.

6. PROPOSED SYSTEM:
• Relying on the definition of two conflicting metrics, namely personalization utility and
privacy risk, for hierarchical user profile, we formulate the problem of privacy-preserving
personalized search as Risk Profile Generalization, with its NP-hardness proved.
• This project proposes a privacy-preserving personalized web search framework UPS, which
can generalize profiles for each query according to user-specified privacy requirements.
• It develops two simple but effective generalization algorithms, GreedyDP and GreedyIL, to
support runtime profiling.While the former tries to maximize the discriminating power
(DP), the latter attempts to minimize the information loss (IL). By exploiting a number of
heuristics, GreedyIL outperforms GreedyDP significantly

7. MODULES:
1. Design and loading image
2. Tools and adjusting the image
3. Filtering
4. Pruning
5. Integrating resizer and filter

8. Input Image (100%) Tools and Adjust the
Image Sobel
Prewitt
Laplace
Pruning
Backward Pass
Forward Pass
Belief Propagation
Img <100%
100% Output Image
Seam carving
Img Resize
SYSTEM FLOW DIAGRAM

9. E-R diagram of sobel filter

10. E-R diagram of prewitt filter

11. E-R diagram of laplace filter
•

12. MODULES DESCRIPTION:
• 1. DESIGN AND LOADING IMAGE
In this module we are designing the screen and image viewer. Next we load the binary image in order to
display the image that demands pruning and image resizing.

13. 2 TOOLS AND ADJUSTING THE IMAGE
• The arrow keys on the keyboard are used to increase or decrease the width and height of the image. The window with the
picture must be the active window on screen for pressing the arrow keys to work. And also to see the adjustment in the screen while
resizing the image.
• (i)Tools
Certain tools are central to the processing of digital images. These include mathematical tools such as convolution, Fourier analysis,
and statistical descriptions, and manipulative tools such as chain codes and run codes.
• (ii)Convolution
In image processing applications, the entire input function is often available for computing every sample of the output function. In
that case, the constraint that each output is the effect of only prior inputs can be relaxed. Convolution amplifies or attenuates each
frequency component of the input independently of the other components.
• (iii)Fourier Analysis
Fourier analysis is a subject area which grew out of the study of Fourier series. The subject began with trying to understand when it
was possible to represent general functions by sums of simpler trigonometric functions.

14. 3. FILTERING
Here we are filtering the image to avoid noise by three algorithms. They are:
 Sobel filter
 Prewitt filter
 Laplace filter

15. SAMPLE SCREEN
FORMS AND REPORTS

18. THANK YOU