Tuesday, 22 July 2014
Supporting Privacy Protection in Personalized Web Search
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. 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.
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:
v The existing methods do not take into account the customization of privacy requirements.
v Privacy issues rising from the lack of protection for such data.
v The existing profile-based PWS do not support runtime profiling.
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 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:
v It gives personalized privacy protection.
v Queries with smaller click-entropies, namely distinct queries, are expected to benefit more from personalization.
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
Operating system : Windows XP.
Coding Language : .Net
Data Base : SQL Server 2005
Tool : VISUAL STUDIO 2008.
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