Protecting data truthfulness in data market using Merkle tree algorithm
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Protecting data truthfulness in data market using Merkle tree algorithm

Project period

03/14/2019 - 04/15/2019

Views

138

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Protecting data truthfulness in data market using Merkle tree algorithm
Protecting data truthfulness in data market using Merkle tree algorithm

Data mining applications have obtained massive growth in today's internet era. It eventually leads to a serious threat to the security of individuals personal and sensitive information. Raw data of millions of users are collected by the service providers through data contributors and they are shared with the data consumers. Existing systems use Privacy-Preserving data mining(PPDM) which modifies the data without compromising the security of the sensitive information contained in the data. we have proposed a new technique called Truthfulness Preserving Data Mining (TPDM) which is structured as encrypt-then-sign fashion. It uses partial homomorphic encryption and identity-based signature that provides batch-verification and data confidentiality.

Why: Problem statement

Data mining applications have obtained massive growth in today internet era. It eventually leads to a serious threat to the security of an individual's personal and sensitive information.

How: Solution description

The main aim of this project to provide an effective survey that guarantees data truthfulness and privacy preservation. The growth of the internet leads to sharing sensitive information with millions of people. Service providers give full authorities to all the data Issued by the d party called service providers.

ALGORITHM:

 A Hash tree or Merkle tree data structure is a tree in which every leaf node is labeled with the hash of a data block for Data verification, Synchronization, and Capital markets
 In the data structure, the hash tree is known as the Merkle tree data structure.

How is it different from competition

In TPDM, the data contributors have to truthfully submit their data, but cannot impersonate others. Besides, the service provider is enforced to truthfully collect and process data.  The proposed execution implemented by the Merkle tree algorithm or merkle tree data structure with the same procedure followed in the TPDM process. 

Who are your customers

Common peoples can use this project for security purposes.

Project Phases and Schedule

  1. Contributors Product Data & Pseudo Identity Creation.
  2. Database Construction by the collector.
  3. Service Selection by the Service Provider.
  4. Results Verification by the Data Consumers.

Resources Required

Software Requirement

Windows 7 and above
JDK 1.7
J2EE 
Tomcat 7.0
MySQL

PROGRAMMING LANGUAGE: 
J2EE (JSP, Servlets),
 JavaScript,
 HTML,
 CSS,
 AJAX.

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