International Conference on Global Trends in Engineering, Technology and Management (ICGTETM-2016)
#1
*2
#
Research Scholar, *Associate Professor
(Department of Computer Engineering , SSBT’s College of Engineering & Technology, Bambhori, Jalgaon
[M.S], INDIA)
Abstract — Personalized web search is an important field for the traditional system for focused information retrieval. It is an attempt to improve personalized web search. User's Profile provides most collected personal data can easily reveal a secret of user’s private life. Privacy problem rising from the lack of protection for such collected personal data. In fact, In PWS services privacy concerns have become important input for performing personalized web search.
A PWS framework also called UPS that can adaptively generalize profiles by queries while respecting privacy requirements specified by user.
Runtime generalization aims at striking a balance between two predictive metrics. This metrics evaluate the personalization and the privacy risk of the generalized profile. Privacy and security of Personal information has the more challenging task in web the major barrier for wide proliferation.
To protect the user privacy in profile-based
PWS, researchers have to two contradicting effects during the search process. On the one side, they attempt to improve the search quality with the personalization utility of the user profile. On the other side, they need to hide the privacy contents existing in the user profile to place the privacy risk under control.
A few previous studies suggest that people are mining. In existing PWS framework evaluate the utility of personalization and the privacy risk of exposing the generalized profile. It solved only privacy problem and this system is not more secure. In proposed system used Secure Decision Tree and Hierarchical user
Profile for enhance privacy and security of personalized web search.
Keywords
—
Security, Privacy protection, personalized web search, utility, risk, profile.
I. INTRODUCTION
The web search engine technology has long become the most important portal for today’s people.
Because They looking for useful information on the web. Personalized web search (PWS) is a category of search techniques at providing best search results, which are tailored for individual user needs. PWS can generally be categorized into two types:
1. Click-log-based Methods : The click-log based methods are important. They impose bias to clicked pages in the user’s query history. Although this method has been perform consistently and considerably well, It is only work on repeated queries from the same user, which is a strong limitation.
2. Profile-Based Method: In contrast, profile-based methods increase the search experience with userinterest models generated from user profiling methods.
Profile-based methods can be more effective for almost all sorts of queries, but It is reported to be unstable under some circumstances. It has demonstrated more effectiveness in improving the effectiveness of web search recently, with increasing usage of personal information to profile its users, which is usually gathered implicitly from browsing history, bookmarks, query history, click-through data, user documents and so on. Unfortunately, such compromise privacy if the personalization by supplying user profile to the search engine better search quality. In case, significant gain can be obtained by personalization at the expense of only a small portion of the user profile, namely a generalize profile. Thus, user privacy can be protected without using compromising the personalized search quality.
In general, there is a tradeoffs between the quality of search and the level of privacy protection achieved from generalization.
Many problems with the existing methods are explained in the following :
1. The existing profile-based PWS method has more effectiveness in improving the quality of web search method. But It do not support runtime profiling. This is the major problem in PWS. A user profile is mostly used for only once offline, and used to personalize all queries from a same user indiscriminatingly. Certainly has disadvantages given the variety of queries.
2. The existing PWS methods do not take into customization of privacy requirements of user. This probably makes some user privacy to be overprotected that’s why others insufficiently protected.
3. Many personalization techniques usually search results with some metrics which require multiple user interactions, such as rank scoring, average rank and so on. They require multiple user iterative user interactions when creating personalized search results of PWS.
.
II. RELATED WORK
Profile-based PWS mainly focus on improving the search utility of PWS. The performance measures of PWS, Normalized Discounted
Cumulative Gain (nDCG) is calculated the effectiveness of an information retrieval system. It is based on a human relevance scale of item-positions in
ISSN: 2231-5381 http://www.ijettjournal.org
Page 212

International Conference on Global Trends in Engineering, Technology and Management (ICGTETM-2016) the result list, and this is, known for its high cost in explicit feedback collection. It is reduced the human involvement in performance measuring, researchers also invent other metrics of personalized web search that rely on including Average Precision (AP) [2], clicking decisions, Rank Scoring [13], and Average
Rank [3], [4]. They use the Average Precision metric.
Y. Xu et al., in [2] presents a scalable way for users to automatically build own user profiles. These profiles based on user’s interests into a hierarchical organization according to specific user interests. Two parameters for specifying privacy requirements are proposed to help the user to choose the content of the user profile information that is exposed to the search engine. Experiments result showed that the user profile improved search quality when compared to standard rankings. More importantly, results verified our hypothesis that a significant increase on search quality can be achieved by only sharing some higherlevel user profile information, which is less sensitive than detailed personal information.
M. Spertta et al., in [3] has given a novel protocol for protecting the users privacy when dealing with a web search engine. It does not require any change in the server side and the server is not allowing to collaborating with the user. The proposed protocol has been implemented to prove its effectivness. But In future will improve the performance of the protocol by reducing the delay introduced.
Alexandre Viejo et al., in [4] given statistical techniques to learn a probabilistic model, and then use this model to generate the partial profile. One main limitation in this technique is that it builds the user profile as a set of attributes is trained through predefined frequent queries. These assumptions are not practical in the context of PWS. It proposed a privacy protection solution for personalized web search based on hierarchical profiles. It is used a userspecified threshold, a generalized profile is obtained in effect as a sub tree of the complete user profile. But, this work does not addressing the user query utility, which is crucial for the service quality of PWS. For comparison, the approach takes the privacy requirement and the query utility into account.
Z. Dou et al., in [5] given provide some preliminary conclusions. Present a large-scale framework for personalized web search based on query logs, and then also evaluate five personalized web search strategies.
By analyzing the results, It find that personalized search has significant improvement over common web search on some queries but it has some effect on other web search queries (e.g., queries with small click entropy). It even harmful search accuracy under some incidents. Furthermore, the click-based personalization strategies perform thee task consistently and considerably well, while profilebased ones are unstable in our experiments. It also reveals that both long term and short term contexts are very important in improving search performance strategies for profile-based personalized search strategies.
X. Shen et al., [6] presented a privacy Protection is the most important part of Personalized Web Search system. Generally they are used two method of privacy protection problems for PWS. Privacy as the identification of an individual user includes in one class.
K. Hafner et al., in [7] given a typical works in the related work of protecting user identifications. They are solved the privacy protection problem on different levels, including the pseudo identity method, no identity method, group identity method, and no personal information.
Y. Zhu et al., in [9] presented the third and fourth levels are not practical due to cost in cryptography.
Therefore, the existing system work only the second level of PWS. Provide online anonymity on user search profiles by providing a group profile of k type of users. Using this approach, the linkage between the query and a single user is broken.
J. Castellı et al., in [10] presented the useless user profile (UUP) protocol is generated to shuffle type of queries among a group of users issue them. As a result provide any entity cannot profile a certain individual.
These works existence of a third-party anonymizer, which is not available over the Internet at large.
A. Viejo et al., in [11] presented a legacy social networks instead of the third party to provide a user distorted profile to the web search technique. In the scheme, every user perform as a web search agency of his or her neighbors. They can decide to submit the user query on basis of who issued it, or forward it to other neighbors.
They has proposed systematically find the issue of privacy preservation in personalized
Web search. Define and analyze four levels of privacy protection engine. They also given the privacy protection of current search systems. But in this system Privacy concern is a serious problem that has become a major barrier for deploying serious personalized web search applications .
Xiao et al., in [12] has given a more important property that differentiate the work from is that provide personalized privacy in Personalized web search. The method of personalized privacy protection is first introduced in Privacy-Preserving Data
Publishing (PPDP). A person can specified the average of privacy protection for her/his personal sensitive values by specifying “guarding nodes” in the sensitive attribute. Motivate by this work, allow users
ISSN: 2231-5381 http://www.ijettjournal.org
Page 213

International Conference on Global Trends in Engineering, Technology and Management (ICGTETM-2016) to customize their privacy needs in their hierarchical user profiles.
III. PROPOSED PERSONALIZED WEB
SEARCH (PWS) FRAMEWORK
4. Personalization:-
In order to incorporate the user personal profile with results returned by a personalized web search engine, It is transformed into a list of weighted terms where a web search calculates a percentage for each of the returned personalized search results.
Figure 1. Architecture of Personalized Web Search
1. Hierarchical User Profile:-
Any personal information such as browsing history, emails, and cookies on a user’s computer could be the data source for user personalized profiles.
This hypothesis is that terms that appear in such documents represent requirements that interest users.
This mainly works on frequent terms limits the dimensionality of the document set, which further generate a clear description of users’ interest in personalized web search. This approach works to build a hierarchical user profile based on terms. In the hierarchical, general terms with higher frequency terms are placed at higher levels of search engine, and specific method with lower frequency are placed at lower levels.
2. Secure Decision Tree:-
Mainly, at each internal node of the decision tree, records are split among the child nodes by a divide criterion, which may contain private terms values. Thus leak private information. Usually, an intermediate general decision tree is larger than the final decision tree, which may leak even more private information. This gives an do not fair advantage to the site that holds the decision tree. It Presented to modify the decision tree into a secure decision tree (SDT) in order to prevent such documents leakage.
3. Measuring Privacy:-
Security is the most important case of people, or organizations to mostly focus for themselves when, how and to what degree data is imparted to others.
Security described is about ensuring clients' close to personal data. Then again, it is clients' control that includes the security.
C ONCLUSION
The Personalized web search framework allowed users to specify customized requirement. Secure decision tree and hierarchical profile provide enhanced privacy and security of personalized web search. It is also performed online generalization technique on user profiles to protect the personal privacy information without compromising the web search quality
In future more sophisticated method to may build the user profile to improve the performance of existing technique.
R EFERENCES
[1] K. Ja¨rvelin and J. Keka¨la¨inen, “IR Evaluation Methods for
Retrieving Highly Relevant Documents,” Proc. 23rd Ann. Int’l
ACM SIGIR Conf. Research and Development Information
Retrieval (SIGIR), pp. 41-48, 2000.
[2] Y. Xu, K. Wang, B. Zhang, and Z. Chen, “Privacy-Enhancing
Personalized Web Search,” Proc. 16th Int’l Conf. World Wide
Web (WWW), pp. 591-600, 2007..
[3] M. Spertta and S. Gach, “Personalizing Search Based on User
Search Histories,” Proc. IEEE/WIC/ACM Int’l Conf. Web
Intelligence (WI), 2005.
[4] F. Qiu and J. Cho, “Automatic Identification of User Interest for
Personalized Search,” Proc. 15th Int’l Conf. World Wide Web
(WWW), pp. 727-736, 2006.
[5] Z. Dou, R. Song, and J.-R. Wen, “A Large-Scale Evaluation and
Analysis of Personalized Search Strategies,” Proc. Int’l Conf.
World Wide Web (WWW), pp. 581-590, 2007..
[6] X. Shen, B. Tan, and C. Zhai, “Privacy Protection in
Personalized Search,” SIGIR Forum, vol. 41, no. 1, pp. 4-17,
2007.
[7] K. Hafner, Researchers Yearn to Use AOL Logs, but They
Hesitate, New York Times, Aug. 2006.
[8] Y. Xu, K. Wang, G. Yang, and A.W.-C. Fu, “Online Anonymity for Personalized Web Services,” Proc. 18th ACM Conf.
Information and Knowledge Management (CIKM), pp. 1497-
1500, 2009.
[9] Y. Zhu, L. Xiong, and C. Verdery, “Anonymizing User Profiles for Personalized Web Search,” Proc. 19th Int’l Conf. World
Wide Web.
[10] J. Castellı´-Roca, A. Viejo, and J. Herrera-Joancomartı´,
“Preservin User’s Privacy in Web Search Engines,” Computer
Comm., vol. 32no. 13/14, pp. 1541-1551, 2009.
[11] A. Viejo and J. Castell_a-Roca, “Using Social Networks to
Distort Users’ Profiles Generated by Web Search Engines,”
Computer Networks, vol. 54, no. 9, pp. 1343-1357, 2010.
[12] X. Xiao and Y. Tao, “Personalized Privacy Preservation,” Proc.
ACM SIGMOD Int’l Conf. Management of Data (SIGMOD),
2006.
ISSN: 2231-5381 http://www.ijettjournal.org
Page 214