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For further details contact: N.RAJASEKARAN B.E M.S 9841091117,9840103301. IMPULSE TECHNOLOGIES, Old No 251, New No 304, 2nd Floor, Arcot road , Vadapalani , Chennai-26. www.impulse.net.in Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com

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Outsourced Similarity Search on Metric Data Assets
Abstract
This paper considers a cloud computing setting in which similarity querying
of metric data is outsourced to a service provider. The data is to be revealed only to
trusted users, not to the service provider or anyone else. Users query the server for
the most similar data objects to a query example. Outsourcing offers the data
owner scalability and a low-initial investment. The need for privacy may be due to
the data being sensitive (e.g., in medicine), valuable (e.g., in astronomy), or
otherwise confidential. Given this setting, the paper presents techniques that
transform the data prior to supplying it to the service provider for similarity queries
on the transformed data. Our techniques provide interesting trade-offs between
query cost and accuracy. They are then further extended to offer an intuitive
privacy guarantee. Empirical studies with real data demonstrate that the techniques
are capable of offering privacy while enabling efficient and accurate processing of
similarity queries.

Your Own Ideas or Any project from any company can be Implemented
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This document outlines a project that proposes a secure provenance transmission technique for sensor networks. The existing system transmits data and provenance over separate channels, but the proposed system requires only a single channel by encoding provenance in packet Bloom filters. This improves security by enabling detection of packet drop attacks, and provenance can be efficiently decoded and verified at the base station. The project describes the objectives, literature review on related work, advantages of the proposed system over existing approaches, and includes an architecture diagram.

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As Cloud Computing becomes prevalent, more and more sensitive information are being centralized into the cloud. For the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Although traditional searchable encryption schemes allow a user to securely search over encrypted data through keywords and selectively retrieve files of interest, these techniques support only exact keyword search. This significant drawback makes existing techniques unsuitable in cloud computing as it is greatly affect system usability, rendering user searching experiences very frustrating and system efficiency very low. In this paper, for the first time we formalize and solve the problem of effective fuzzy keyword search over encrypted cloud while maintaining keyword privacy. In our solution, we exploit edit distance to quantify keyword similarity and develop new advanced technique on constructing fuzzy keyword sets which greatly reduces the storage and representation overheads. In this way, we show that our proposed solution is secure and privacy preserving, while realizing the goal of fuzzy keyword search.

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Sharing data that contains personally identifiable or sensitive information, such as medical records, always has privacy and security implications. The issues can become rather complex when the methods of access can vary, and accurate individual data needs to be provided whilst mass data release for specific purposes (for example for medical research) also has to be catered for. Although various solutions have been proposed to address the different aspects individually, a comprehensive approach is highly desirable. This paper presents a solution for maintaining the privacy of data released en masse in a controlled manner, and for providing secure access to the original data for authorized users. The results show that the solution is provably secure and maintains privacy in a more efficient manner than previous solutions

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This document proposes a secure multi-keyword ranked search scheme over encrypted cloud data that allows for dynamic operations like deletion and insertion of documents. It constructs a special tree-based index structure and uses a "Greedy Depth-first Search" algorithm to provide efficient search. The scheme utilizes a secure kNN algorithm to encrypt index and query vectors while still allowing accurate relevance scoring between encrypted vectors. It aims to achieve sub-linear search time and flexible handling of document updates.

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This document proposes a system called privacy-preserving multi-keyword ranked search over encrypted cloud data (MRSE). Existing searchable encryption systems only support single-keyword or boolean keyword search without result ranking. The proposed MRSE system allows a user to search for multiple keywords and returns documents ranked by relevance. It establishes privacy requirements and uses an efficient "coordinate matching" semantic to quantify document similarity based on keyword matches. The system architecture includes modules for data owners to encrypt and upload files, for users to search and download encrypted files, and for ranking search results.

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IMPULSE_TECHNOLOGY

This document discusses energy-efficient strategies for cooperative multichannel MAC protocols. It introduces Distributed Information SHaring (DISH), which helps nodes make decisions by sharing information with neighboring nodes. This approach was shown to significantly increase throughput but had not addressed energy efficiency. The paper proposes two strategies: in-situ energy conscious DISH which uses existing nodes, and altruistic DISH which uses additional nodes called altruists. Evaluation shows altruistic DISH conserves 40-80% of energy, maintains throughput advantages, and more than doubles cost efficiency compared to protocols without this strategy. In-situ energy conscious DISH is only suitable in limited scenarios.

IMPULSE_TECHNOLOGY

CTVS is a novel data extraction and alignment method that combines tag and value similarity to extract data from query result pages. It first identifies and segments query result records in the pages and aligns them into a table with data values from the same attribute in the same column. CTVS handles cases where records are not contiguous due to auxiliary information and any nested structures within records. It also designs a new record alignment algorithm that aligns attributes pairwise and holistically using tag and value similarity. Experimental results show CTVS achieves high precision and outperforms existing methods.

IMPULSE_TECHNOLOGY

The document discusses a new algorithm for topic mining over asynchronous text sequences. The algorithm aims to explore correlations between multiple related text sequences that may have different time stamps. It consists of two alternating steps: 1) extracting common topics from sequences based on adjusted time stamps, and 2) adjusting time stamps according to the discovered topic time distributions. The approach is evaluated on research papers and news articles, demonstrating effectiveness in identifying topics across asynchronously published documents.

IMPULSE_TECHNOLOGY

The document presents a new approach called TSCAN for temporally summarizing topics from a collection of documents. TSCAN first derives the major themes of a topic from the eigenvectors of a temporal block association matrix. It then extracts significant events and their summaries for each theme by examining the eigenvectors. Finally, it associates the extracted events based on their temporal closeness and context similarity to form an evolution graph of the topic. Experiments on the TDT4 corpus show that temporal summaries generated by TSCAN present topics in a comprehensible form and are superior to existing summarization methods based on human references.

IMPULSE_TECHNOLOGY

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1. Impulse Technologies Beacons U to World of technology 044-42133143, 98401 03301,9841091117 ieeeprojects@yahoo.com www.impulse.net.in Outsourced Similarity Search on Metric Data Assets Abstract This paper considers a cloud computing setting in which similarity querying of metric data is outsourced to a service provider. The data is to be revealed only to trusted users, not to the service provider or anyone else. Users query the server for the most similar data objects to a query example. Outsourcing offers the data owner scalability and a low-initial investment. The need for privacy may be due to the data being sensitive (e.g., in medicine), valuable (e.g., in astronomy), or otherwise confidential. Given this setting, the paper presents techniques that transform the data prior to supplying it to the service provider for similarity queries on the transformed data. Our techniques provide interesting trade-offs between query cost and accuracy. They are then further extended to offer an intuitive privacy guarantee. Empirical studies with real data demonstrate that the techniques are capable of offering privacy while enabling efficient and accurate processing of similarity queries. Your Own Ideas or Any project from any company can be Implemented at Better price (All Projects can be done in Java or DotNet whichever the student wants) 1

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