Tools for privacy preserving distributed data mining
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TLDR
This paper presents some components of a toolkit of components that can be combined for specific privacy-preserving data mining applications, and shows how they can be used to solve several Privacy preserving data mining problems.Abstract:
Privacy preserving mining of distributed data has numerous applications. Each application poses different constraints: What is meant by privacy, what are the desired results, how is the data distributed, what are the constraints on collaboration and cooperative computing, etc. We suggest that the solution to this is a toolkit of components that can be combined for specific privacy-preserving data mining applications. This paper presents some components of such a toolkit, and shows how they can be used to solve several privacy-preserving data mining problems.read more
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Journal ArticleDOI
L-diversity: Privacy beyond k-anonymity
TL;DR: This paper shows with two simple attacks that a \kappa-anonymized dataset has some subtle, but severe privacy problems, and proposes a novel and powerful privacy definition called \ell-diversity, which is practical and can be implemented efficiently.
Proceedings ArticleDOI
L-diversity: privacy beyond k-anonymity
TL;DR: This paper shows with two simple attacks that a \kappa-anonymized dataset has some subtle, but severe privacy problems, and proposes a novel and powerful privacy definition called \ell-diversity, which is practical and can be implemented efficiently.
[서평]「Applied Cryptography」
TL;DR: The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity.
Journal ArticleDOI
Privacy-preserving data publishing: A survey of recent developments
TL;DR: This survey will systematically summarize and evaluate different approaches to PPDP, study the challenges in practical data publishing, clarify the differences and requirements that distinguish P PDP from other related problems, and propose future research directions.
Proceedings ArticleDOI
Smooth sensitivity and sampling in private data analysis
TL;DR: This is the first formal analysis of the effect of instance-based noise in the context of data privacy, and shows how to do this efficiently for several different functions, including the median and the cost of the minimum spanning tree.
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