Motion Detection to Preserve Personal Privacy from Surveillance Data using Contrary Motion
Pavan Kumar Vadrevu1, Sri Krishna Adusumalli2, Vamsi Krishna Mangalapalli3
1Pavan Kumar Vadrevu, Research Scholar, Department of Computer Science and Engineering, Centurion University of Technology and Management, Paralakhemundi, Orissa.
2Sri Krishna Adusumalli, Associate Professor, Department of Information Technology, Shri Vishnu Engineering College for Women, Bhimavaram, (AP), India.
3Vamsi Krishna Mangalapalli, Professor, Department of CSE, Chaitanya Institute of Science and Technology, Kakinada.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3892-3895 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8584038620/2020©BEIESP | DOI: 10.35940/ijrte.F8584.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Internet of Things network today naturally is one of the huge quantities of devices from sensors linked through the communication framework to give value added service to the society and mankind. That allows equipment to be connected at anytime with anything rather using network and service. By 2020 there will be 50 to 100 billion devices connected to Internet and will generate heavy data that is to be analyzed for knowledge mining is a forecast. The data collected from individual devices of IoT is not going to give sufficient information to perform any type of analysis like disaster management, sentiment analysis, and smart cities and on surveillance. Privacy and Security related research increasing from last few years. IoT generated data is very huge, and the existing mechanisms like k- anonymity, l-diversity and differential privacy were not able to address these personal privacy issues because the Internet of Things Era is more vulnerable than the Internet Era . To solve the personal privacy related problems researchers and IT professionals have to pay more attention to derive policies and to address the key issues of personal privacy preservation, so the utility and trade off will be increased to the Internet of Things applications. Personal Privacy Preserving Data Publication (PPPDP) is the area where the problems are identified and fixed in this IoT Era to ensure better personal privacy.
Keywords: Personal Privacy, Surveillance Data, Motion Detection.
Scope of the Article: Sequential, Parallel And Distributed Algorithms And Data Structures.