research-article

Data mining without data: a novel approach to privacy-preserving collaborative distributed data mining

Authors:

Vikas Ashok,

Ravi MukkamalaAuthors Info & Claims

WPES '11: Proceedings of the 10th annual ACM workshop on Privacy in the electronic society

Pages 159 - 164

https://doi.org/10.1145/2046556.2046578

Published: 17 October 2011 Publication History

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Abstract

With the proliferation of organizations that independently collect various types of data, with the growing awareness of corporations and public to keep their sensitive data private, and with the ever-increasing need of government and corporate policy makers to learn the behavior of their customers, there is a definite demand for data mining services even when the data owners refuse to provide their data directly. In the past, techniques such as random perturbation were used by data owners prior to sharing the data with a third-party data miner. But, as already proven, even these techniques are prone to privacy-violation.

In this paper, we take a completely different approach---each data owner derives association rules locally, sanitizes them if necessary, and sends them to a third-party data miner. The data miner collects local rules from all data owners, regenerates an estimate of global data, and performs global data mining. We suggest schemes to reduce the generation of spurious rules, a possible outcome of data generation from rules. The proposed method is illustrated using an example of association rule data mining. We are currently in the process of formalizing some of the underlying techniques and to make them more efficient.

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Cited By

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Hewage USinha RNaeem M(2023)Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature reviewArtificial Intelligence Review10.1007/s10462-023-10425-356:9(10427-10464)Online publication date: 22-Feb-2023
https://doi.org/10.1007/s10462-023-10425-3
Shah MJoshi H(2016)Prognosis using distributed data classification with privacy preserving: A novel approach2016 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE)10.1109/WIECON-ECE.2016.8009081(32-35)Online publication date: Dec-2016
https://doi.org/10.1109/WIECON-ECE.2016.8009081
Ashok VNavuluri KAlhafdhi AMukkamala R(2015)Dataless Data MiningProceedings of the 2015 12th International Conference on Information Technology - New Generations10.1109/ITNG.2015.102(615-620)Online publication date: 13-Apr-2015
https://dl.acm.org/doi/10.1109/ITNG.2015.102
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Data mining without data: a novel approach to privacy-preserving collaborative distributed data mining

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Published In

WPES '11: Proceedings of the 10th annual ACM workshop on Privacy in the electronic society

October 2011

192 pages

ISBN:9781450310024

DOI:10.1145/2046556

General Chair:
Yan Chen
Northwestern University, USA
,
Program Chair:
Jaideep Vaidya
Rutgers University, USA

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 17 October 2011

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CCS'11: the ACM Conference on Computer and Communications Security

October 17, 2011

Illinois, Chicago, USA

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Overall Acceptance Rate 106 of 355 submissions, 30%

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Hewage USinha RNaeem M(2023)Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature reviewArtificial Intelligence Review10.1007/s10462-023-10425-356:9(10427-10464)Online publication date: 22-Feb-2023
https://doi.org/10.1007/s10462-023-10425-3
Shah MJoshi H(2016)Prognosis using distributed data classification with privacy preserving: A novel approach2016 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE)10.1109/WIECON-ECE.2016.8009081(32-35)Online publication date: Dec-2016
https://doi.org/10.1109/WIECON-ECE.2016.8009081
Ashok VNavuluri KAlhafdhi AMukkamala R(2015)Dataless Data MiningProceedings of the 2015 12th International Conference on Information Technology - New Generations10.1109/ITNG.2015.102(615-620)Online publication date: 13-Apr-2015
https://dl.acm.org/doi/10.1109/ITNG.2015.102
Adhikari AAdhikari JPedrycz WAdhikari AAdhikari JPedrycz W(2013)IntroductionData Analysis and Pattern Recognition in Multiple Databases10.1007/978-3-319-03410-2_1(1-19)Online publication date: 7-Dec-2013
https://doi.org/10.1007/978-3-319-03410-2_1

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Analysis of Association rule in Data Mining

Research and Application of Association Rules Methods in Data Mining for Commercial Sales Analysis

EXTRACT: new extraction algorithm of association rules from frequent itemsets

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