Book Chapter10.1007/11510888_60
Inference on distributed data clustering
Josenildo Costa da Silva,Matthias Klusch +1 more
- 09 Jul 2005
- pp 610-619
6
TL;DR: In this article, the authors present a measure of inference risk as a function of reconstruction precision and number of colluders in a distributed data mining group, which is a distributed clustering algorithm designed to provide mining results while preserving confidentiality of original data.
read more
Abstract: In this paper we address confidentiality issues in distributed data clustering, particularly the inference problem. We present a measure of inference risk as a function of reconstruction precision and number of colluders in a distributed data mining group. We also present KDEC-S, which is a distributed clustering algorithm designed to provide mining results while preserving confidentiality of original data. The underlying idea of our algorithm is to use an approximation of density estimation such that it is not possible to reconstruct the original data with better probability than some given level.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A Survey of Association Rule Hiding Methods for Privacy
Vassilios S. Verykios,Aris Gkoulalas-Divanis +1 more
- 01 Jan 2008
TL;DR: A taxonomy and a survey of recent approaches that have been applied to the association rule hiding problem are presented and interesting future directions in this research body are enumerated.
85
Inference in distributed data clustering
TL;DR: KDEC-S algorithm for distributed data clustering, which is shown to provide mining results while preserving confidentiality of original data, is presented and a confidentiality framework is presented with which to state the confidentiality level of K DEC-S.
27
Collaborative Clustering: How to Select the Optimal Collaborators?
Parisa Rastin,Guénaël Cabanes,Nistor Grozavu,Younès Bennani +3 more
- 01 Dec 2015
TL;DR: Experimental analysis on four real vector data-sets showed that the diversity between collaborators impact the quality of the collaboration and it was shown that the internal indexes of quality are a good estimator of the increase of quality due to the collaboration.
20
PartSOM: A Framework for Distributed Data Clustering Using SOM and K-Means
Flavius L. Gorgonio,José Alfredo Ferreira Costa +1 more
- 01 Apr 2010
TL;DR: Cluster analysis can be defined as the process of partition data into a certain number of clusters (or groups) of similar objects, where each group consists of similarObjects amongst themselves and different from the objects of the other groups.
Impact of Learners’ Quality and Diversity in Collaborative Clustering
Parisa Rastin,Basarab Matei,Guénaël Cabanes,Nistor Grozavu,Younès Bennani +4 more
- 01 Apr 2019
TL;DR: The impact of the quality and the diversity of the potential learners to the quality of the collaboration for topological collaborative clustering algorithms based on the learning of a Self-Organizing Map (SOM) is analyzed.
4
References
Privacy-preserving data mining
Rakesh Agrawal,Ramakrishnan Srikant +1 more
- 16 May 2000
TL;DR: This work considers the concrete case of building a decision-tree classifier from training data in which the values of individual records have been perturbed and proposes a novel reconstruction procedure to accurately estimate the distribution of original data values.
3.5K
•Proceedings Article
An efficient approach to clustering in large multimedia databases with noise
Alexander Hinneburg,Daniel A. Keim +1 more
- 27 Aug 1998
TL;DR: A new algorithm to clustering in large multimedia databases called DENCLUE (DENsity-based CLUstEring) is introduced, which has a firm mathematical basis, has good clustering properties in data sets with large amounts of noise, allows a compact mathematical description of arbitrarily shaped clusters in high-dimensional data sets and is significantly faster than existing algorithms.
Privacy Preserving Data Mining
Yehuda Lindell,Benny Pinkas +1 more
- 20 Aug 2000
TL;DR: In this paper, the authors introduce the concept of privacy preserving data mining, where two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information.
Privacy-preserving distributed mining of association rules on horizontally partitioned data
Murat Kantarcioglu,Chris Clifton +1 more
TL;DR: In this paper, the authors address secure mining of association rules over horizontally partitioned data. And they incorporate cryptographic techniques to minimize the information shared, while adding little overhead to the mining task.
Limiting privacy breaches in privacy preserving data mining
Alexandre V. Evfimievski,Johannes Gehrke,Ramakrishnan Srikant +2 more
- 09 Jun 2003
TL;DR: This paper presents a new formulation of privacy breaches, together with a methodology, "amplification", for limiting them, and instantiate this methodology for the problem of mining association rules, and modify the algorithm from [9] to limit privacy breaches without knowledge of the data distribution.
Related Papers (5)
Huidong Jin,Kwong-Sak Leung,Man Leung Wong +2 more
- 01 Jan 2002
R. Bulli Babu,G. Snehal,P. Aditya Satya Kiran +2 more
- 01 Mar 2017
[...]
Maria-Florina Balcan,Travis Dick,Manuel Lang +2 more
- 30 Apr 2020