Icics2017087 by ASDF Administartor

International Conference on Intelligent Computing and Systems

International Conference on Intelligent Computing and Systems 2017 [ICICS 2017]

ISBN Website Received Article ID

978-81-933235-5-7 icics.asia 10 – January – 2017 ICICS087

VOL eMail Accepted eAID

01 icics@asdf.res.in 28 - January – 2017 ICICS.2017.087

Discovery and Prevention Techniques for Discrimination 1

T Sangeetha1, K Sorna Deepika2, V Srimathi3 Assistant Professor, UG Scholar, Department of Information Technology, Sri Krishna College of Technology, India 2,3

Abstract: Discrimination is action that denies social participation or human rights to categories of people based on prejudice. It includes unjust or unequal treatment of different groups of people, especially on the grounds of race, religion, age, or sex. Discrimination is one of the negative social perceptions about data mining. Automated decision making is the main aim of the data mining such as classification rule mining etc. Historical training dataset is used for creating decision models. If the training dataset is modified unfairly on the discriminatory attributes, discriminated decisions may occur. So the antidiscrimination techniques are introduced with the data mining. In proposed anti-discrimination system, discriminations are discovered using the elift, slift measures. The identified discriminations are transformed into discrimination free dataset in discrimination prevention phase. Discrimination prevention is done by rule protection and rule generalization methods. Discrimination prevention phase constructs the system that does not lead to discriminatory decisions even if the original training datasets are inherently biased. So the training and outsourced datasets are cleaned and produce a non-discriminatory (legitimate) classification rules. The performance of proposed anti-discrimination methods is evaluated using degree of discrimination removal and impacts of methods are measured in terms of information loss.

ISBN Website Received Article ID

978-81-933235-5-7 icics.asia 10 – January – 2017 ICICS088

VOL eMail Accepted eAID

01 icics@asdf.res.in 28 - January – 2017 ICICS.2017.088

Free Rider Isolation Using Aggregated Trust Model Including Weightage Considerations 1

P Arockia Mary1, M Radhakrishnan2 Associate Professor, Department of CSE, 2Director/IT, Sudharsan Engineering College, India

Abstract: One common issue in P2P overlay networks is Free-Riding in which some peers only receive files from other peers but never or seldom give files to others. Free-riders affect the strength and efficiency of the overlay and hence need to be checked and isolated from the network. Methods are available to identify and isolate free-riders using trust models. Most of such models use the feedback from other peers which have inherent issues like malice and unreliability. Behavioural trust models that permit self-evaluation by peers with built-in measures for fairness have received less attention. An Aggregated Trust model that evaluates the trust value of a peer from its own benefit and contribution history is presented in this paper. The model takes into account not only the size and number of the files exchanged but also the weightage of each file based on its size and popularity. A comparison with the results of aggregated trust models show that the proposed model is more efficient in isolating the free-riders.

This paper is prepared exclusively for International Conference on Intelligent Computing and Systems 2017 [ICICS 2017] which is published by ASDF International, Registered in London, United Kingdom under the directions of the Editor-in-Chief Dr M Sivaraja and Editors Dr. Daniel James, Dr. Kokula Krishna Hari Kunasekaran and Dr. Saikishore Elangovan. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage, and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honoured. For all other uses, contact the owner/author(s). Copyright Holder can be reached at copy@asdf.international for distribution.