Data integration and data privacy through “Pay-As-You-Go” approach

Abstract

Data Analytics has taken important and demanding problems in the research areas such as computer science, biology, medicine, finance, and homeland security. This research paper has resolved the problem of Entity resolution(ER) which recognizes the database records, which referred to the same real-world entity. The latest explosion of data made ER a impeach problem in a large range of applications. This paper proposed a scalable ER approach, used on-board datasets. Our latest approaches are simple because they consider either the entire ER process or the function, which are matching, and merging records as a black box procedure and used in a large range of ER applications. Pay-as-you-go approach for ER was a limit on the resources (e.g., work, runtime). This made the maximum progress as possible as required. This paper suggests scalable ER methods and new ER functionalities that have been not studied in the previous. Entity Resolution as a black-box operation provides general mechanisms which be used across applications. Further, the issue of managing information leakage, where one must try to avoid important bits of data from resolved by Entity Resolution, to sage against the loss of data privacy. As more of our sensitive data gets unprotected to various merchants, health care providers, employers, social sites and so on, there is a large chance that an adversary can "connect the dots" and piece together our data, which leads to even more damage of privacy. Thus to measure the quantifying data leakage, we use "disinformation" as a device which containing data leakage. © 2019, Research Trend. All rights reserved.