The document proposes a privacy-preserving personalized web search framework called UPS. It aims to generalize user profiles for each query according to user-specified privacy requirements, while balancing personalization utility and privacy risk. Two algorithms, GreedyDP and GreedyIL, are developed to support runtime profile generalization. An online mechanism is also provided to decide whether personalizing a query would be beneficial without compromising privacy. Experiments show the effectiveness and efficiency of the UPS framework in achieving personalized search results while preserving user privacy.
Supporting privacy protection in personalized web searchLeMeniz Infotech
Supporting privacy protection in personalized web search
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.
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Supporting privacy protection in personalized web searchLeMeniz Infotech
Supporting privacy protection in personalized web search
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.
1.supporting privacy protection in personalized web search..9440480873 ,proje...RamaKrishnaReddyKona
supporting privacy protection in personalized web search..new projects java and web based projects available low cost ..plz dial 9440480873 krishna reddy...low price provided projects
SUPPORTING PRIVACY PROTECTION IN PERSONALIZED WEB SEARCHnikhil421080
Personalized web search (PWS) has demonstrated its effectiveness in improving the quality of various search services on the Internet. However, evidence shows 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 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.
Web search engines (e.g. Google, Yahoo, Microsoft Live Search, etc.) are widely used to find certain data among a huge amount of information in a minimal amount of time. These useful tools also pose a privacy threat to users. Web search engines profile their users on the basis of past searches submitted by them. In the proposed system, we can implement the String Similarity Match Algorithm (SSM Algorithm) for improving better search quality results. To address this privacy threat, current solutions propose new mechanisms that introduce a high cost in terms of computation and communication. Personalized search is a promising way to improve the accuracy of web searches. However, effective personalized search requires collecting and aggregating user information, which often raises serious concerns of privacy infringement for many users. Indeed, these concerns have become one of the main barriers to deploying personalized search applications, and how to do privacy-preserving personalization is a great challenge. In this, we propose and try to resist adversaries with broader background knowledge, such as richer relationship among topics. Richer relationship means we generalize the user profile results by using the background knowledge which is going to store in history. Through this, we can hide the user search results. By using this mechanism, we can achieve privacy.
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.
Secure Protection in Customized Web SearchEditor IJMTER
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. The solutions to PWS can generally be categorized into
two types, namely click-log-based methods and profile-based ones. The click-log based methods are
straightforward— they simply impose bias to clicked pages in the user’s query history. Although this
strategy has been demonstrated to perform consistently and considerably well, it can only work on repeated
queries from the same user, which is a strong limitation confining its applicability. In contrast, profile-based
methods improve the search experience with complicated user-interest models generated from user profiling
techniques. Profile-based methods can be potentially effective for almost all sorts of queries, but are
reported to be unstable under some circumstances.
PMSE captures the users’ preferences in the form of concepts by mining their click through data.
Classification of location information
-Content concept
-Location concept
Users’ locations (positioned by GPS) are also used.
The user preferences are organized in an ontology-based, multi-facet user profile.
The client- collects and stores locally clickthrough data to protect privacy.
At server- concept extraction, training and reranking.
Privacy issue – is taken care by restricting the information in the user profile.
We prototype PMSE on the Google Android platform.
Results show that PMSE significantly improves the precision comparing to the baseline.
JPJ1426 Supporting Privacy Protection in Personalized Web Searchchennaijp
We are good IEEE java projects development center in Chennai and Pondicherry. We guided advanced java technologies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
A Least Square Approach ToAnlayze Usage Data For Effective Web PersonalizationIDES Editor
Web server logs have abundant information about
the nature of users accessing it. The analysis of the users
current interestbased on the navigational behavior may help
the organizationsto guide the users in their browsing activity
and obtain relevantinformation in a shorter span of time
[1].Web usage mining is used to discover interesting user
navigationpatterns and can be applied to many real-world
problems, such as improving Web sites/pages,
makingadditional topic or product recommendations, user/
customer behavior studies, etc [23].Web usage mining, in
conjunction with standard approaches to personalization
helps to address some of the shortcomings of these
techniques, including reliance on subjective lack of
scalability, poor performance, user ratings and sparse
data[2,3,4,5,6]. But, it is not sufficient to discover patterns
from usage data for performing the personalization tasks.
It is necessary to derive a good quality of aggregate usage
profiles which indeed will help to devise efficient
recommendation for web personalization [11, 12, 13].Also
the unsupervised and competitive learning algorithms has
help to efficiently cluster user based access patterns by
mining web logs [19, 20, 24].
This paper presents and experimentally evaluates a
technique for finely tuninguser clusters based on similar
web access patterns on their usage profiles by approximating
through least square approach. Each cluster is having users
with similar browsing patterns. These clusters are useful
in web personalization so that it communicates better with
its users.Experimental results indicate thatusing the
generated aggregate usage profiles with approximating
clusters through least square approach effectively
personalize at early stages of user visits to a site without
deeper knowledge about them.
Privacy Preservation And Data Security In Location Based ServicesEditorJST
In this paper, a solution for privacy preservation and data security is presented. Privacy over the internet can be defined as the ability to decide what information one discloses or withholds about a person over the internet, who can access such information and for what reason a person’s information may or may not be accessed. The problem is stated as follows: (i) a client needs to inquire a database which contains some authorized and sensitive data and does not want to disclose himself to the server because of privacy concerns (ii) the owner of the database i.e the server, does not want to simply give out its data to all users. The server needs to have some control over its information, since the information is its asset. In this paper, a two stage approach is proposed to achieve secure solution for both user and the server. The first step is accomplished using Oblivious Transfer and second step is accomplished using Data Retrieval phase. And, a security model has been devised, which includes encryption and hashing algorithm for providing data security.
An introductory presentation about the current state of personalization in (Web) search for Bibliotekarforbundet's series of 'gå-hjem-møder'. Presented on May 17, 2016 at Aalborg University Copenhagen.
Location-Based Service (LBS) becomes increasingly popular with the dramatic growth of smartphones and social network services (SNS), and its context-rich functionalities attract considerable users.
SUPPORTING PRIVACY PROTECTION IN PERSONALIZED WEB SEARCHnikhil421080
Personalized web search (PWS) has demonstrated its effectiveness in improving the quality of various search services on the Internet. However, evidence shows 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 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.
Web search engines (e.g. Google, Yahoo, Microsoft Live Search, etc.) are widely used to find certain data among a huge amount of information in a minimal amount of time. These useful tools also pose a privacy threat to users. Web search engines profile their users on the basis of past searches submitted by them. In the proposed system, we can implement the String Similarity Match Algorithm (SSM Algorithm) for improving better search quality results. To address this privacy threat, current solutions propose new mechanisms that introduce a high cost in terms of computation and communication. Personalized search is a promising way to improve the accuracy of web searches. However, effective personalized search requires collecting and aggregating user information, which often raises serious concerns of privacy infringement for many users. Indeed, these concerns have become one of the main barriers to deploying personalized search applications, and how to do privacy-preserving personalization is a great challenge. In this, we propose and try to resist adversaries with broader background knowledge, such as richer relationship among topics. Richer relationship means we generalize the user profile results by using the background knowledge which is going to store in history. Through this, we can hide the user search results. By using this mechanism, we can achieve privacy.
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.
Secure Protection in Customized Web SearchEditor IJMTER
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. The solutions to PWS can generally be categorized into
two types, namely click-log-based methods and profile-based ones. The click-log based methods are
straightforward— they simply impose bias to clicked pages in the user’s query history. Although this
strategy has been demonstrated to perform consistently and considerably well, it can only work on repeated
queries from the same user, which is a strong limitation confining its applicability. In contrast, profile-based
methods improve the search experience with complicated user-interest models generated from user profiling
techniques. Profile-based methods can be potentially effective for almost all sorts of queries, but are
reported to be unstable under some circumstances.
PMSE captures the users’ preferences in the form of concepts by mining their click through data.
Classification of location information
-Content concept
-Location concept
Users’ locations (positioned by GPS) are also used.
The user preferences are organized in an ontology-based, multi-facet user profile.
The client- collects and stores locally clickthrough data to protect privacy.
At server- concept extraction, training and reranking.
Privacy issue – is taken care by restricting the information in the user profile.
We prototype PMSE on the Google Android platform.
Results show that PMSE significantly improves the precision comparing to the baseline.
JPJ1426 Supporting Privacy Protection in Personalized Web Searchchennaijp
We are good IEEE java projects development center in Chennai and Pondicherry. We guided advanced java technologies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
A Least Square Approach ToAnlayze Usage Data For Effective Web PersonalizationIDES Editor
Web server logs have abundant information about
the nature of users accessing it. The analysis of the users
current interestbased on the navigational behavior may help
the organizationsto guide the users in their browsing activity
and obtain relevantinformation in a shorter span of time
[1].Web usage mining is used to discover interesting user
navigationpatterns and can be applied to many real-world
problems, such as improving Web sites/pages,
makingadditional topic or product recommendations, user/
customer behavior studies, etc [23].Web usage mining, in
conjunction with standard approaches to personalization
helps to address some of the shortcomings of these
techniques, including reliance on subjective lack of
scalability, poor performance, user ratings and sparse
data[2,3,4,5,6]. But, it is not sufficient to discover patterns
from usage data for performing the personalization tasks.
It is necessary to derive a good quality of aggregate usage
profiles which indeed will help to devise efficient
recommendation for web personalization [11, 12, 13].Also
the unsupervised and competitive learning algorithms has
help to efficiently cluster user based access patterns by
mining web logs [19, 20, 24].
This paper presents and experimentally evaluates a
technique for finely tuninguser clusters based on similar
web access patterns on their usage profiles by approximating
through least square approach. Each cluster is having users
with similar browsing patterns. These clusters are useful
in web personalization so that it communicates better with
its users.Experimental results indicate thatusing the
generated aggregate usage profiles with approximating
clusters through least square approach effectively
personalize at early stages of user visits to a site without
deeper knowledge about them.
Privacy Preservation And Data Security In Location Based ServicesEditorJST
In this paper, a solution for privacy preservation and data security is presented. Privacy over the internet can be defined as the ability to decide what information one discloses or withholds about a person over the internet, who can access such information and for what reason a person’s information may or may not be accessed. The problem is stated as follows: (i) a client needs to inquire a database which contains some authorized and sensitive data and does not want to disclose himself to the server because of privacy concerns (ii) the owner of the database i.e the server, does not want to simply give out its data to all users. The server needs to have some control over its information, since the information is its asset. In this paper, a two stage approach is proposed to achieve secure solution for both user and the server. The first step is accomplished using Oblivious Transfer and second step is accomplished using Data Retrieval phase. And, a security model has been devised, which includes encryption and hashing algorithm for providing data security.
An introductory presentation about the current state of personalization in (Web) search for Bibliotekarforbundet's series of 'gå-hjem-møder'. Presented on May 17, 2016 at Aalborg University Copenhagen.
Location-Based Service (LBS) becomes increasingly popular with the dramatic growth of smartphones and social network services (SNS), and its context-rich functionalities attract considerable users.
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The size of the Internet enlarging as per to grow the users of search providers continually demand search
results that are accurate to their wishes. Personalized Search is one of the options available to users in
order to sculpt search results based on their personal data returned to them provided to the search
provider. This brings up fears of privacy issues however, as users are typically anxious to revealing
personal info to an often faceless service provider along the Internet. This work proposes to administer
with the privacy issues surrounding personalized search and discusses ways that privacy can be improved
so that users can get easier with the dismissal of their personal information in order to obtain more precise
search results.
User profiling is a fundamental component of any personalization applications. Most existing user profiling strategies are based on objects that users are interested in (i.e., positive preferences), but not the objects that users dislike (i.e., negative preferences). In this paper, we focus on search engine personalization and develop several concept-based user profiling methods that are based on both positive and negative preferences. We evaluate the proposed methods against our previously proposed personalized query clustering method. Experimental results show that profiles which capture and utilize both of the user’s positive and negative preferences perform the best. An important result from the experiments is that profiles with negative preferences can increase the separation between similar and dissimilar queries. The separation provides a clear threshold for an agglomerative clustering algorithm to terminate and improve the overall quality of the resulting query clusters.
Similar to Supporting privacy protection in personalized web search (20)
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Efficient Instant-Fuzzy Search with Proximity Ranking
System finds answers to a query instantly while user types in keywords character-by-character.
Fuzzy search improves user search experiences by finding relevant answers with keywords similar to query keywords.
A main computational challenge in this paradigm is the high speed requirement
At the same time, we also need good ranking functions that consider the proximity of keywords to compute relevance scores
The previous systems were able to recommend results based on just previously typed characters kept in cache module.
Most of the times Previous Search Log might be useful to make recommendation system more faster!
Relevance to user query along with users intentions could be mined easily.
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Key aggregate cryptosystem for scalable data sharing in cloud storage using pairng based cryptography. We used JPBC tool to implement Key Aggregate cryptosystem.
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Hierarchical Digital Twin of a Naval Power SystemKerry Sado
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Supporting privacy protection in personalized web search
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 userspecified 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 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.
We develops Computer Engineering Projects for BE/ME students. For any kind of support you may live
chat with us at www.ocularsystems.in or call us on 020 30858066 or
Mail Us: info@ocularsystems.in
Our Address: Swagat Corner Building, Near Narayani Dham Temple, Katraj, Pune-46 (Maharashtra)
2. Existing System
The existing profile-based Personalized Web Search do not support runtime profiling. A
user profile is typically generalized for only once offline, and used to personalize all queries
from a same user indiscriminatingly. Such “one profile fits all” strategy certainly has drawbacks
given the variety of queries. One evidence reported in is that 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.
For example, in, all the sensitive topics are detected using an absolute metric called surprisal
based on the information theory, assuming that the interests with less user document support are
more sensitive. However, this assumption can be doubted with a simple counterexample: If a
user has a large number of documents about “sex,” the surprisal of this topic may lead to a
conclusion that “sex” is very general and not sensitive, despite the truth which is opposite.
Unfortunately, few prior work can effectively address individual privacy needs during the
generalization.
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. This paradigm is, however,
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.
Disadvantage:
We develops Computer Engineering Projects for BE/ME students. For any kind of support you may live
chat with us at www.ocularsystems.in or call us on 020 30858066 or
Mail Us: info@ocularsystems.in
Our Address: Swagat Corner Building, Near Narayani Dham Temple, Katraj, Pune-46 (Maharashtra)
3. All the sensitive topics are detected using an absolute metric called surprisal based on the
information theory.
Proposed System:
We propose a privacy-preserving personalized web search framework UPS, which can
generalize profiles for each query according to user-specified privacy requirements. 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 itsNP-hardness proved.
We develop 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.
We provide an inexpensive mechanism for the client to decide whether to personalize a
query in UPS. This decision can be made before each runtime profiling to enhance the stability
of the search results while avoid the unnecessary exposure of the profile.
Advantages:
1. It enhances the stability of the search quality.
2. It avoids the unnecessary exposure of the user profile.
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4. Architecture:
Enhanced
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5. MODULES”
1. Profile-Based Personalization.
2. Privacy Protection in PWS System.
3. Generalizing User Profile.
4. Online Decision.
Modules Description
1. Profile-Based Personalization
This paper introduces an approach to personalize digital multimedia content based on
user profile information. For this, two main mechanisms were developed: a profile
generator that automatically creates user profiles representing the user preferences, and a
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6. content-based recommendation algorithm that estimates the user's interest in unknown
content by matching her profile to metadata descriptions of the content. Both features are
integrated into a personalization system.
2. Privacy Protection in PWS System
We propose a PWS framework called UPS that can generalize profiles in for each
query according to user-specified privacy requirements. Two predictive metrics are
proposed to evaluate the privacy breach risk and the query utility for hierarchical user
profile. We develop two simple but effective generalization algorithms for user profiles
allowing for query-level customization using our proposed metrics. We also provide an
online prediction mechanism based on query utility for deciding whether to personalize a
query in UPS. Extensive experiments demonstrate the efficiency and effectiveness of our
framework.
3. Generalizing User Profile
The generalization process has to meet specific prerequisites to handle the user
profile. This is achieved by preprocessing the user profile. At first, the process initializes
the user profile by taking the indicated parent user profile into account. The process adds
the inherited properties to the properties of the local user profile. Thereafter the process
loads the data for the foreground and the background of the map according to the
described selection in the user profile.
Additionally, using references enables caching and is helpful when considering an
implementation in a production environment. The reference to the user profile can be
used as an identifier for already processed user profiles. It allows performing the
customization process once, but reusing the result multiple times. However, it has to be
made sure, that an update of the user profile is also propagated to the generalization
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7. process. This requires specific update strategies, which check after a specific timeout or a
specific event, if the user profile has not changed yet. Additionally, as the generalization
process involves remote data services, which might be updated frequently, the cached
generalization results might become outdated. Thus selecting a specific caching strategy
requires careful analysis.
4. Online Decision
The profile-based personalization contributes little or even reduces the search
quality, while exposing the profile to a server would for sure risk the user’s privacy. To
address this problem, we develop an online mechanism to decide whether to personalize a
query. The basic idea is straightforward. if a distinct query is identified during
generalization, the entire runtime profiling will be aborted and the query will be sent to
the server without a user profile.
System Configuration:-
H/W System Configuration:-
Processor - Pentium –III
Speed - 1.1 Ghz
RAM - 256 MB (min)
Hard Disk - 20 GB
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8. Floppy Drive - 1.44 MB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
S/W System Configuration:-
Operating System :Windows95/98/2000/XP
Application Server : Tomcat5.0/6.X
Front End : HTML, Java, Jsp
Scripts : JavaScript.
Server side Script : Java Server Pages.
Database : Mysql
Database Connectivity : JDBC.
CONCLUSION
This paper presented a client-side privacy protection framework called UPS for
personalized web search. UPS could potentially be adopted by any PWS that captures user
profiles in a hierarchical taxonomy. The framework allowed users to specify customized privacy
requirements via the hierarchical profiles. In addition, UPS also performed online generalization
on user profiles to protect the personal privacy without compromising the search quality. We
proposed two greedy algorithms, namely GreedyDP and GreedyIL, for the online generalization.
Our experimental results revealed that UPS could achieve quality search results while preserving
We develops Computer Engineering Projects for BE/ME students. For any kind of support you may live
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9. user’s customized privacy requirements. The results also confirmed the effectiveness and
efficiency of our solution.
REFERENCES
[1] 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.
[2] J. Teevan, S.T. Dumais, and E. Horvitz, “Personalizing Search via Automated Analysis of
Interests and Activities,” Proc. 28th Ann. Int’l ACM SIGIR Conf. Research and Development in
Information Retrieval (SIGIR), pp. 449-456, 2005.
[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] B. Tan, X. Shen, and C. Zhai, “Mining Long-Term Search History to Improve Search
Accuracy,” Proc. ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD),
2006.
[5] K. Sugiyama, K. Hatano, and M. Yoshikawa, “Adaptive Web Search Based on User Profile
Constructed without any Effort from Users,” Proc. 13th Int’l Conf. World Wide Web (WWW),
2004.
Scope:
To protect user privacy in profile-based PWS, researchers have to consider two
contradicting effects during the search process. On the one hand, they attempt to improve the
search quality with the personalization utility of the user profile. On the other hand, 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 willing to compromise privacy if the
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10. personalization by supplying user profile to the search engine yields better search quality. In an
ideal case, significant gain can be obtained by personalization at the expense of only a small (and
less-sensitive) portion of the user profile, namely a generalized profile. Thus, user privacy can be
protected without compromising the personalized search quality. In general, there is a tradeoff
between the search quality and the level of privacy protection achieved from generalization.
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