NDLI: Privacy-Preserving Two-Party Distributed Association Rules Mining on Horizontally Partitioned Data

Content Provider	IEEE Xplore Digital Library
Author	Feng Zhang Chunming Rong Gansen Zhao Jinxia Wu Xiangning Wu
Copyright Year	2013
Description	Author affiliation: Fac. of Sci. & Technol., Univ. of Stavanger, Stavanger, Norway (Chunming Rong) \|\| Sch. of Comput. Sci., South China Normal Univ., Guangzhou, China (Gansen Zhao) \|\| Sch. of Comput. Sci., China Univ. of Geosci., Wuhan, China (Feng Zhang; Jinxia Wu; Xiangning Wu)
Abstract	In many applications, data mining has to be done in distributed data scenarios. In such situations, data owners may be concerned with the misuse of data, hence, they do not want their data to be mined, especially when these contain sensitive information. Privacy-preserving Data Mining (PPDM) aims to protect data privacy in the course of data mining. Privacy preserving distributed association rules mining protocols have been developed for horizontally partitioned data scenarios with more than two participating parties. However, they depend on a secure multi-party summary and union computation, which cannot guarantee security while the number of participating parties is two. We use commutative encryption and design a secure division computation protocol as the core techniques to implement the protocols for the privacy-preserving two-party distributed mining of association rule mining. The protocols' security and performance are analyzed.
Sponsorship	IEEE Comput. Soc.
Starting Page	633
Ending Page	640
File Size	425648
Page Count	8
File Format	PDF
e-ISBN	9781479928309
DOI	10.1109/CLOUDCOM-ASIA.2013.87
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data privacy Protocols Itemsets Distributed Association Rules Privacy-Preserving Encryption Association rules Data mining
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

DARM: a privacy-preserving approach for distributed association rules mining on horizontally-partitioned data

Frequent Closed Itemset Mining with Privacy Preserving for Distributed Databases

Privacy-preserving distributed mining of association rules on horizontally partitioned data (2002).

Privacy Preserving Clustering on Horizontally Partitioned Data

2 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, TO APPEAR Privacy-preserving Distributed Mining of Association Rules on Horizontally Partitioned Data

An Enhanced Scheme for Privacy-Preserving Association Rules Mining on Horizontally Distributed Databases

A Privacy Preserving Algorithm for Mining Distributed Association Rules

An approach to mining association rules in horizontally distributed databases with anonymous ID assignment

Secure Mining of Association Rules in Horizontally Distributed Databases

Privacy-Preserving Two-Party Distributed Association Rules Mining on Horizontally Partitioned Data

Similar Documents

DARM: a privacy-preserving approach for distributed association rules mining on horizontally-partitioned data

Frequent Closed Itemset Mining with Privacy Preserving for Distributed Databases

Privacy-preserving distributed mining of association rules on horizontally partitioned data (2002).

Privacy Preserving Clustering on Horizontally Partitioned Data

2 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, TO APPEAR Privacy-preserving Distributed Mining of Association Rules on Horizontally Partitioned Data

An Enhanced Scheme for Privacy-Preserving Association Rules Mining on Horizontally Distributed Databases

A Privacy Preserving Algorithm for Mining Distributed Association Rules

An approach to mining association rules in horizontally distributed databases with anonymous ID assignment

Secure Mining of Association Rules in Horizontally Distributed Databases

Privacy-Preserving Two-Party Distributed Association Rules Mining on Horizontally Partitioned Data