Skip to Main content Skip to Navigation
Conference papers

A Scalable and Efficient Privacy Preserving Global Itemset Support Approximation Using Bloom Filters

Abstract : Several secure distributed data mining methods have been proposed in the literature that are based on privacy preserving set operation mechanisms. However, they are limited in the scalability of both the size and the number of data owners (sources). Most of these techniques are primarily designed to work with two data owners and extensions to handle multiple owners are either expensive or infeasible. In addition, for large datasets, they incur substantial communication/computation overhead due to the use of cryptographic techniques. In this paper, we propose a scalable privacy-preserving protocol that approximates global itemset support, without employing any cryptographic mechanism. We also present some emperical results to demonstrate the effectiveness of our approach.
Document type :
Conference papers
Complete list of metadata

Cited literature [10 references]  Display  Hide  Download

https://hal.inria.fr/hal-01284874
Contributor : Hal Ifip <>
Submitted on : Tuesday, March 8, 2016 - 11:12:37 AM
Last modification on : Tuesday, August 13, 2019 - 11:00:07 AM
Long-term archiving on: : Sunday, November 13, 2016 - 10:43:49 AM

File

978-3-662-43936-4_26_Chapter.p...
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Vikas Ashok, Ravi Mukkamala. A Scalable and Efficient Privacy Preserving Global Itemset Support Approximation Using Bloom Filters. 28th IFIP Annual Conference on Data and Applications Security and Privacy (DBSec), Jul 2014, Vienna, Austria. pp.382-389, ⟨10.1007/978-3-662-43936-4_26⟩. ⟨hal-01284874⟩

Share

Metrics

Record views

372

Files downloads

515