research-article

A comparative study on innovative approaches for privacy-preservation in knowledge discovery

Authors:

Gerald QuirchmayrAuthors Info & Claims

ICIME 2017: Proceedings of the 9th International Conference on Information Management and Engineering

Pages 120 - 127

https://doi.org/10.1145/3149572.3149586

Published: 09 October 2017 Publication History

Abstract

Confronting with growing size of data and pressure of extracting useful knowledge in different manners made privacy preserving a crucial subject. This major is even more important especially in big data environment that implements knowledge discovery and data mining for producing beneficial information. Beside the inner importance aspect of privacy of personal data, the efficiency of the approaches of preserving privacy is a special factor. This is because of the overheads that injected by privacy preserving methods in decreasing the accuracy of end results of data mining. Soft computing is a general name of a group of logic based methods that have several usages. Its recent usage is in privacy preserving in big data. In this paper, a comprehensive survey of different regular methods of privacy preserving for KDD and Data Mining presented and then reasons of why soft computing methods can be a substitute for Privacy Preserving in these environments are discussed. Beside the analysis and discussion of merit and shortcomings of approaches, a conceptual framework for state of the art of privacy-preserving represented and provides research gaps and future works.

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Cited By

Chaudhary NGupta VSandhir KGupta RChhabra SSingh A(2022)Privacy Preserving Ensemble Learning Classification Model for Mental Healthcare2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC)10.1109/PDGC56933.2022.10053268(513-518)Online publication date: 25-Nov-2022
https://doi.org/10.1109/PDGC56933.2022.10053268
Oprescu AMisdorp Svan Elsen K(2022)Energy cost and accuracy impact of k-anonymity2022 International Conference on ICT for Sustainability (ICT4S)10.1109/ICT4S55073.2022.00018(65-76)Online publication date: Jun-2022
https://doi.org/10.1109/ICT4S55073.2022.00018
Verykios VStavropoulos EKrasadakis PSakkopoulos E(2021)Frequent itemset hiding revisited: pushing hiding constraints into miningApplied Intelligence10.1007/s10489-021-02490-4Online publication date: 16-Jun-2021
https://doi.org/10.1007/s10489-021-02490-4
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Index Terms

A comparative study on innovative approaches for privacy-preservation in knowledge discovery
1. Security and privacy
  1. Security services
    1. Privacy-preserving protocols

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ICIME 2017: Proceedings of the 9th International Conference on Information Management and Engineering

October 2017

233 pages

ISBN:9781450353373

DOI:10.1145/3149572

Copyright © 2017 ACM.

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University of Salford: University of Salford

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Published: 09 October 2017

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ICIME 2017

ICIME 2017: 2017 9th International Conference on Information Management and Engineering

October 9 - 11, 2017

Barcelona, Spain

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Cited By

Chaudhary NGupta VSandhir KGupta RChhabra SSingh A(2022)Privacy Preserving Ensemble Learning Classification Model for Mental Healthcare2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC)10.1109/PDGC56933.2022.10053268(513-518)Online publication date: 25-Nov-2022
https://doi.org/10.1109/PDGC56933.2022.10053268
Oprescu AMisdorp Svan Elsen K(2022)Energy cost and accuracy impact of k-anonymity2022 International Conference on ICT for Sustainability (ICT4S)10.1109/ICT4S55073.2022.00018(65-76)Online publication date: Jun-2022
https://doi.org/10.1109/ICT4S55073.2022.00018
Verykios VStavropoulos EKrasadakis PSakkopoulos E(2021)Frequent itemset hiding revisited: pushing hiding constraints into miningApplied Intelligence10.1007/s10489-021-02490-4Online publication date: 16-Jun-2021
https://doi.org/10.1007/s10489-021-02490-4
Amiri FQuirchmayr GKieseberg PWeippl EBertone A(2019)Towards Data Anonymization in Data Mining via Meta-heuristic ApproachesData Privacy Management, Cryptocurrencies and Blockchain Technology10.1007/978-3-030-31500-9_3(39-48)Online publication date: 20-Sep-2019
https://doi.org/10.1007/978-3-030-31500-9_3

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