Maximizing Data Utility by using Data Anonymization Technique

Data Anonymization is one of the techniques for achieving the database privacy. In this method of data anonymization the facility of hiding the identification factor from the other users. Hence the feature of this method is used for the modification purpose only and that will remove the identities of person and appears table in same way. It will help for maintaining the risk factor. This approach can be increased data utility tradeoff in an organization. Scalability and privacy risk are the main factors regarding any database over an organization. Here this are two factor can be help to maximizing the data utility as well as minimizing the risk by using differential privacy preserving method. At the time of released data differential privacy preserving mechanism support for individual data hiding , by adding the noise and disclose for the secondary purpose. DOI: 10.17762/ijritcc2321-8169.150615

Ontology-Based Quality Evaluation of Value Generalization Hierarchies for Data Anonymization

In privacy-preserving data publishing, approaches using Value Generalization Hierarchies (VGHs) form an important class of anonymization algorithms. VGHs play a key role in the utility of published datasets as they dictate how the anonymization of the data occurs. For categorical attributes, it is imperative to preserve the semantics of the original data in order to achieve a higher utility. Despite this, semantics have not being formally considered in the specification of VGHs. Moreover, there are no methods that allow the users to assess the quality of their VGH. In this paper, we propose a measurement scheme, based on ontologies, to quantitatively evaluate the quality of VGHs, in terms of semantic consistency and taxonomic organization, with the aim of producing higher-quality anonymizations. We demonstrate, through a case study, how our evaluation scheme can be used to compare the quality of multiple VGHs and can help to identify faulty VGHs.Comment: 18 pages, 7 figures, presented in the Privacy in Statistical Databases Conference 2014 (Ibiza, Spain

SECRETA: A System for Evaluating and Comparing RElational and Transaction Anonymization algorithms

Publishing data about individuals, in a privacy-preserving way, has led to a large body of research. Meanwhile, algorithms for anonymizing datasets, with relational or transaction attributes, that preserve data truthfulness, have attracted significant interest from organizations. However, selecting the most appropriate algorithm is still far from trivial, and tools that assist data publishers in this task are needed. In response, we develop SECRETA, a system for analyzing the effectiveness and efficiency of anonymization algorithms. Our system allows data publishers to evaluate a specific algorithm, compare multiple algorithms, and combine algorithms for anonymizing datasets with both relational and transaction attributes. The analysis of the algorithm(s) is performed, in an interactive and progressive way, and results, including attribute statistics and various data utility indicators, are summarized and presented graphically

Sales Management Portal

GSU Sales portal is a portal which provides a sales pipeline through an application with a conceptual design for the database where the sales staff enters client’s information into this portal which keeps track of contacts and activities, client information and incoming requests from future clients. The portal has different modules to function efficiently. User Management: This allows addition of new managers and manager could add more sales login and user logins. Opportunity management: This allows addition of new opportunity type. Proposal management: This module will contain all the new proposals and existing proposals. Projects view: This module will contain all the projects and sales users can add new projects into this module which can also be viewed. Technical Specification: ASP.NET Database: Microsoft SQL Server 2008 Tools: Microsoft Visual Studio 2015, Microsoft SQL Serve

Privacy Preservation using T-Closeness with Numerical Attributes

Data mining is a process that is used to retrieve the knowledgeable data from the large dataset. Information imparting around two associations will be basic done a large number requisition zones. As people are uploading their personal data over the internet, however the data collection and data distribution may lead to disclosure of their privacy. So, preserving the privacy of the sensitive data is the challenging task in data mining. Many organizations or hospitals are analyzing the medical data to predict the disease or symptoms of disease. So, before sharing data to other organization need to protect the patient personal data and for that need privacy preservation. In the recent year�s privacy preserving data mining has being received a large amount of attention in the research area. To achieve the expected goal various methods have been proposed. In this paper, to achieve this goal a pre-processing technique i.e. k-means clustering along with anonymization technique i.e. k-anonymization and t-closeness and done analysis which techniques achieves more information gain

Bottom up approach to manage data privacy policy through the front end filter paradigm

An increasing number of business services for private companies and citizens are accomplished trough the web and mobile devices. Such a scenario is characterized by high dynamism and untrustworthiness, as a large number of applications exchange different kinds of data. This poses an urgent need for effective means in preserving data privacy. This paper proposes an approach, inspired to the front-end trust filter paradigm, to manage data privacy in a very flexible way. Preliminary experimentation suggests that the solution could be a promising path to follow for web-based transactions which will be very widespread in the next future

Bottom up approach to manage data privacy policy through the front end filter paradigm

An increasing number of business services for private companies and citizens are accomplished trough the web and mobile devices. Such a scenario is characterized by high dynamism and untrustworthiness, as a large number of applications exchange different kinds of data. This poses an urgent need for effective means in preserving data privacy. This paper proposes an approach, inspired to the front-end trust filter paradigm, to manage data privacy in a very flexible way. Preliminary experimentation suggests that the solution could be a promising path to follow for web-based transactions which will be very widespread in the next future