Selected Privacy Research Topics in the ARES Project: An Overview

Manjón, Jesús A.; Domingo-Ferrer, Josep

doi:10.1007/978-3-319-09885-2_2

Cited by 4 publications

(1 citation statement)

References 35 publications

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“…However, it fails to maintain privacy preservation and discrimination prevention simultaneously. In [5] designed to protect the privacy of the users from a web search engine that tries to protect them. The system provided a distorted user profile to the internet search engine because some of each user's queries are submitted by his/her friends in the social network.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Discrimination Prevention and Rule Protection Algorithms Avoid Direct and Indirect Data Discrimination in Web Mining

Babu¹,

Pushpa²

2018

IJIES

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Discrimination is one of the most important challenging tasks in web mining due to its many legal and ethical features in social media and enterprise based industries. There are an enormous amount of anti-discrimination measures available to prevent discrimination such as using some features like race, religion, gender, nationality, disability, marital status, and age besides situations like employment and training, access to public services, credit, insurance, etc. Practically, those systems are not possible to use in industries due to large datasets. Indirect discrimination contains a set of rules or techniques which are not explicitly specifying discriminatory features, deliberately or accidentally and could create unfair decisions. Existing systems have low classification accuracy and data loss with high discrimination data detection time. To overcome these limitations, an Efficient Discrimination Prevention and Rule Protection (EDPRP) approach has been proposed for removing the discrimination and protects the rule without damaging the data quality. The proposed system designing pre-processing discrimination prevention approach and specify the different features and represent to deal with direct or indirect discrimination. EDPRP is capable of preventing Indirect and direct discrimination, and it allows automatic and routine collection of large amounts of data from the public. In EDPRP, the discrimination prevention model is based on partial data sets as part of the automated decision making. Based on Experimental evaluations, proposed method improves 8% (percentages) of Support and 8 ms (milliseconds) of Execution Time compared than existing methods.

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Section: Related Workmentioning

confidence: 99%