Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Global Effoets for reducing the accidents are growing faster but the accidents happened is growing are the fastest one. An analysis clealy shows around 30 percent of the accidents happening around the road is mainly because of the drunk and drive. Through survelliance camera and alcohol measuring meters we can monitor the drunk and drive incidents around the city. But cars are speedy at highways where deploying security person for checking drunken drivers is unimaginobly difficult. To solve this in the prescribed work we deploy the sensors in every tool booth to monitor the incoming vehicles and stop the drunken drivers to further drive on the highway. This device is built from existing data acquired from the drunken drivers which is analyzed with machine learning techniques and mathematical data analysis models. © 2019 IEEE.

VIT Unitversity

Department of Software and Systems Engineering

School of Information Technology and Engineering

Vellore Campus

Department of Smart Computing

2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)

Sensing Drunken Drivers Using Data Science

Preservation of privacy is a significant aspect of data mining and as the secrecy of sensitive information must be maintained while sharing the data among different untrusted parties. There are many application is suffering from vulnerable, data leakage, data misuse, and sensitive data disclosure issues. To protect the privacy of sensitive data without losing the usability of data, various techniques have been used in privacy-preserving data mining (PPDM). Some of the approaches are available to maintain the tight privacy, but they fail to minimize the execution time and error rate. The main objective of the article is to contribute and retrieve the data with minimal classification error and execution time with enhanced privacy. To overcome the issues, the paper introduces the Secure Data Contribution Retrieval algorithm (SDCRA) to fulfill the current issues. Proposed algorithms define a privacypolicy and arrange the security based on requirements. This design applies the privacy based on the compatibility of applications. This approach is capable of satisfying the accuracy constraints for multiple datasets. It also considers the efficient data extraction with a good ranking of attributes in tables. Here, proposed SDCRA is compared with existing approaches namely as Perturbation, singular value decomposition (SVD), Singular Value Decomposition data Perturbation (SVD+DP), K-anonymity with DecisionTree (KA+DT)[] for Cancer, HIV, Diabetes dataset. Based on experimental result proposed approach performs well regarding success rate, error rate and system execution time compare than existing methods. Proposed approach improves Success Rate 1.83% reduces the Error Rate 2.33% and minimizes the system execution time 2 seconds

Department of Information Technology

International Journal of Intelligent Engineering and Systems

A Credential Data Privacy Preserving in Web Environment Using Secure Data Contribution Retrieval Algorithm

Data mining produces a large amount of data that needs to be analyzed and prioritized in order to extract useful information from it and gain more knowledge from the data. The aim of data mining tools is to find useful patterns, techniques and models from the available of large data. Hence knowledge about various data mining techniques may contain private information about people or business. The data in data mining is vulnerable to data hackers and employees to take advantage of the situation and misuse data. Preservation of privacy is a significant aspect of data mining and as secrecy of sensitive information must be maintained while sharing the data among different un-trusted parties. To protect the privacy of sensitive data without losing the usability of data, various techniques have been used in privacy preserving data mining (PPDM) to achieve the goal. The aim of this paper is to present privacy preserving data mining techniques. Current application systems are suffering several data privacy during online. There is required some work for content privacy on web. This work plans to work on privacy of web content during data extraction and clustering.

Research Journal of Pharmacy and Technology

Privacy preserving in data mining technical: A review

Wireless Networks

Secure authentication and integrity techniques for randomized secured routing in WSN

2011 Third International Conference on Communication Systems and Networks (COMSNETS 2011)

Data integrity proofs in cloud storage