This document proposes a personalized web search (PWS) framework called UPS that protects user privacy during search. UPS can generalize user profiles to different levels based on privacy requirements while balancing personalization utility and privacy risk. It presents two greedy algorithms, GreedyDP and GreedyIL, for runtime profile generalization and an online mechanism for deciding when to personalize queries. Experiments show UPS effectively protects privacy while maintaining personalization benefits and GreedyIL outperforms GreedyDP in efficiency.

1. SUPPORTING PRIVACY PROTECTION IN
PERSONALIZED WEB SEARCH
ABSTRACT:
Personalized web search (PWS) has demonstrated its
effectiveness in improving the quality of various search services
on the Internet. However, evidences show that users’ reluctance
to disclose their private information during search has become a
major barrier for the wide proliferation of PWS. We study
privacy protection in PWS applications that model user
preferences as hierarchical user profiles. We propose a PWS
framework called UPS that can adaptively generalize profiles by
queries while respecting user specified privacy requirements.
Our runtime generalization aims at striking a balance between
two predictive metrics that evaluate the utility of personalization
and the privacy risk of exposing the generalized profile. We
present two greedy algorithms, namely GreedyDP and
GreedyIL, for runtime generalization. We also provide an online
prediction mechanism for deciding whether personalizing a

2. query is beneficial. Extensive experiments demonstrate the
effectiveness of our framework. The experimental results also
reveal that GreedyIL significantly outperforms GreedyDP in
terms of efficiency.
EXISTING SYSTEM:
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. Such irrelevance is largely due to the enormous
variety of users’ contexts and backgrounds, as well as the
ambiguity of texts. Personalized web search (PWS) is a general
category of search techniques aiming at providing better search
results, which are tailored for individual user needs. As the
expense, user information has to be collected and analyzed to
figure out the user intention behind the issued query.
DISADVANTAGES OF EXISTING SYSTEM:

3.   The existing methods do not take into account the
customization of privacy requirements.
 Privacy issues rising from the lack of protection for such
data.
 The existing profile-based PWS do not support runtime
profiling.
PROPOSED SYSTEM:
We propose a novel energy-aware routing algorithm, called
reliable minimum energy cost routing (RMECR). RMECR finds
energy efficient and reliable routes that increase the operational
lifetime of the network. In the design of RMECR, we use an in-depth
and detailed analytical model of the energy consumption
of nodes. RMECR is proposed for networks with hop-by-hop
(HBH) retransmissions providing link layer reliability, and
networks with E2E retransmissions providing E2E reliability.
HBH retransmission is supported by the medium access control
(MAC) layer (more precisely the data link layer) to increase

4. reliability of packet transmission over wireless links.
Nevertheless, some MAC protocols such as CSMA and MACA
may not support HBH retransmissions. In such a case, E2E
retransmission could be used to ensure E2E reliability.
ADVANTAGES OF PROPOSED SYSTEM:
It gives personalized privacy protection.
Queries with smaller click-entropies, namely distinct queries,
are expected to benefit more from personalization.
SYSTEM ARCHITECTURE:

5. SYSTEM CONFIGURATION:-
HARDWARE REQUIREMENTS:-
Processor - Pentium –IV
Speed - 1.1 Ghz
RAM - 512 MB(min)
Hard Disk - 40 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - LCD/LED
SOFTWARE REQUIREMENTS:
Operating system : Windows XP.
Coding Language : .Net
Data Base : SQL Server 2005
Tool : VISUAL STUDIO 2008.

6. REFERENCE:
Lidan Shou, He Bai, Ke Chen, and Gang Chen, “Supporting Privacy Protection
in Personalized Web Search” IEEE TRANSACTIONS ON KNOWLEDGE
AND DATA ENGINEERING, VOL. 26, NO. 2, FEBRUARY 2014

7. REFERENCE:
Lidan Shou, He Bai, Ke Chen, and Gang Chen, “Supporting Privacy Protection
in Personalized Web Search” IEEE TRANSACTIONS ON KNOWLEDGE
AND DATA ENGINEERING, VOL. 26, NO. 2, FEBRUARY 2014