Informatics Publishing Limited

Syed Akram ¹, S. Selvakumar ², J. J. Lohith ², B. R. Shambhavi ³, M. Indiramma ²

Affiliations
1 Dept. of CSE, BMSCE, Karnataka, IN
2 Dept. of CSE, BMSCE, Bangalore, Karnataka, IN
3 Dept. of ISE, BMSCE, Bangalore, Karnataka, IN

Abstract

Industry Institute Interaction has different perspective and different reasons to interact with each other. Both are mutually benefited from this interaction. Academia is the main source of knowledge for industry. In general, role of Industry is an important factor for research and outcome based education in engineering institutions. Outcome Based Education is incomplete without successful interaction between Industry and Engineering Institution. This paper present the activities under MOU'S signed between BMS college of Engineering and other IT Industries. These activities aim to achieve the different Programme Outcomes.

Keywords

Outcome Based Education, Programme Outcomes, Course Outcomes, Industry Institute Interaction, Memorandum of Understanding.

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M. Indiramma ¹, K. R. Anandakumar ²

Affiliations
1 BMSCE, Bangalore, IN
2 Department of CSE, SJBIT, Bangalore, IN

Abstract

The distribution of information, authority, personnel, and resources are inevitable with the development of Information and Communication Technologies (ICT) in modern organization. Among different organizational models, Virtual Organization (VO) and Virtual Team (VT) are becoming increasingly important research topic in the field of Multi Agent Systems (MAS). Virtual teams are teams whose members are geographically distributed, requiring them to work together through electronic means with minimal, or in extreme circumstances, no face-to-face interaction. In such situation social interaction forms a vital part of any team work and also interpersonal trust plays an important role. In our previous work we presented an agent based framework for collaborative decision making in VT and trust computation model. Trust computation involves direct interactions and recommendations from others. In this paper we are focusing on estimation of general reputation of agents at organization level.

Keywords

Bayesian Network, Collaborative Decision Making, Multi Agent System (MAS), Trust.

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N. P. Nethravathi ¹, Vaibhav J. Desai ², P. Deepa Shenoy ², M. Indiramma ³, K. R. Venugopal ²

Affiliations
1 Visvesvaraya Technological University, Belagavi-590 018, IN
2 University Visvesvaraya College of Engineering, Bangalore University, Bangalore, IN
3 BMS College of Engineering, Bangalore-560019, IN

Abstract

With the onset of the digital revolution, organizations are increasingly maintaining a huge amount of information on their databases and use data mining tools to extract useful information for their business intelligence. The problem with the availability of the digital information is the scarce privacy leakage. In many business domains, leakage of personal information of the client either directly or through data mining tools can lead to loss of competitive edge of the company, loss of revenue and customer churn. Companies are pushing for encryption and other data transformation methods to keep the data private. But mining tools which invoke algorithms like clustering, classification etc. may not work properly on the transformed data. In this paper, we analyze the privacy preserving data mining solutions and privacy leakage in them through indirect means. The main objective of this paper is to identify the open areas of research on privacy-preserving data mining.

Keywords

Transformation Strategy, Privacy Preserving Data Mining, Cryptography, Wavelet Transformation, Correlation Analysis.

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N. P. Nethravathi ¹, Prasanth G. Rao ¹, Chaitra C. Vaidya ², S. Geethanjali ², P. Madhura ², K. Neha Nandan ², P. Deepa Shenoy ², M. Indiramma ³, K. R. Venugopal ²

Affiliations
1 Visvesvaraya Technological University, Belagavi - 590 018, Karnataka, IN
2 University Visvesvaraya College of Engineering, Bangalore University, Bangalore - 560001, Karnataka, IN
3 BMS College of Engineering, Bangalore - 560019, Karnataka, IN

Abstract

Objectives: Preservation of privacy is a significant aspect of data mining. The main objective of PPDM is to hide or provide privacy to certain sensitive information so that they can be protected from unauthorized parties or intruders. Methods/ Statistical Analysis: Though privacy is achieved by hiding the sensitive or private data, it will affect the data mining algorithms in knowledge extraction, so an effective method or strategy is required to provide privacy to the data and simultaneously protecting the quality of data mining algorithms. Instead of removing or encrypting sensitive or private data, we make use of data transformation strategies that keep the statistical, semantic and heuristic nature of data while protecting the sensitive or private data. Findings: In this paper we studied the technical feasibility of realizing Privacy Preserving Data Mining. In the proposed work, Correlation Based Transformation Strategy for Privacy Preserving Data Mining is used for ordinal data. We apply the method on few datasets namely soybean, Breast Cancer, Nursery dataset and Car dataset. We tabulate the end results applying the proposed strategy on both the original and the transformed dataset and observe correlation difference, Information Entropy and Classification Accuracy with different machine learning algorithms and Clustering Quality. Application/Improvements: As an improvement, the proposed work can be extended by use of vector marking techniques where these techniques help in increasing the efficiency by avoiding unauthorised access to the information.

Keywords

Correlation Analysis, Nominal Data, Ordinal Data, Privacy Preserving Data Mining, Transformation Strategy.

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