NDLI: An Attack on the Privacy of Sanitized Data that Fuses the Outputs of Multiple Data Miners

Content Provider	IEEE Xplore Digital Library
Author	Sramka, M. Safavi-Naini, R. Denzinger, J.
Copyright Year	2009
Abstract	Data sanitization has been used to restrict re-identification of individuals and disclosure of sensitive information from published data. We propose an attack on the privacy of the published sanitized data that simply fuses outputs of multiple data miners that are applied to the sanitized data. That attack is practical and does not require any background or additional information. We use a number of experiments to show scenarios where an adversary can combine outputs of multiple miners using a simple fusion strategy to increase their success chance of breaching privacy of individuals whose data is stored in the database. The fusion attack provides a powerful method of breaching privacy in the form of partial disclosure, for both anonymized and perturbed data. It also provides an effective way of approximating predictions of the best miner (a miner that provides the best results among all considered miners) when this miner cannot be determined.
Starting Page	130
Ending Page	137
File Size	279774
Page Count	8
File Format	PDF
ISBN	9781424453849
DOI	10.1109/ICDMW.2009.28
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Data privacy Fuses Databases Conferences Packaging Data engineering Data mining Informatics Statistics
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

An attack on the privacy of sanitized data that fuses the outputs of multiple data miners.

A survey on privacy preserving data mining

An Efficient Sequential Pattern Mining Algorithm Based on the 2-Sequence Matrix

A New Scheme on Privacy-Preserving Distributed Decision-Tree Mining

Privacy-preserving data mining on data grids in the presence of malicious participants

OntoDM: An Ontology of Data Mining

Privacy Preserving Association Rules by Using Greedy Approach

FiVaTech: Page-Level Web Data Extraction from Template Pages

Privacy-preserving frequent pattern mining across private databases

An Attack on the Privacy of Sanitized Data that Fuses the Outputs of Multiple Data Miners

Similar Documents

An attack on the privacy of sanitized data that fuses the outputs of multiple data miners.

A survey on privacy preserving data mining

An Efficient Sequential Pattern Mining Algorithm Based on the 2-Sequence Matrix

A New Scheme on Privacy-Preserving Distributed Decision-Tree Mining

Privacy-preserving data mining on data grids in the presence of malicious participants

OntoDM: An Ontology of Data Mining

Privacy Preserving Association Rules by Using Greedy Approach

FiVaTech: Page-Level Web Data Extraction from Template Pages

Privacy-preserving frequent pattern mining across private databases

An Attack on the Privacy of Sanitized Data that Fuses the Outputs of Multiple Data Miners