• Publications
  • Influence
Privacy-preserving distributed mining of association rules on horizontally partitioned data
TLDR
We address secure mining of association rules over horizontally partitioned data. Expand
  • 1,037
  • 72
  • PDF
Tools for privacy preserving distributed data mining
TLDR
We propose a toolkit of components that can be combined for specific privacy-preserving data mining applications. Expand
  • 945
  • 62
  • PDF
Privacy-preserving k-means clustering over vertically partitioned data
TLDR
A method for k-means clustering when different sites contain different attributes for a common set of entities, but learn nothing about the attributes at other sites. Expand
  • 718
  • 46
  • PDF
SEMINT: A tool for identifying attribute correspondences in heterogeneous databases using neural networks
TLDR
This paper provides theoretical background and implementation details of SEMINT. Expand
  • 422
  • 29
  • PDF
Privacy-preserving data integration and sharing
TLDR
Integrating data from multiple sources has been a longstanding challenge in the database community. Expand
  • 195
  • 16
  • PDF
Secure set intersection cardinality with application to association rule mining
TLDR
This paper presents an efficient protocol for securely determining the size of set intersection and shows how this can be used to generate association rules where multiple parties have different (and private) information about the same set of individuals. Expand
  • 219
  • 15
  • PDF
Privacy-preserving Naïve Bayes classification
TLDR
This paper brings privacy-preservation to that baseline, presenting protocols to develop a Naïve Bayes classifier on both vertically as well as horizontally partitioned data. Expand
  • 192
  • 15
  • PDF
Using unknowns to prevent discovery of association rules
TLDR
We introduce a method for selectively removing individual values from a database to prevent the discovery of a set of rules, while preserving the data for other applications. Expand
  • 369
  • 13
  • PDF
Hiding the presence of individuals from shared databases
TLDR
We show that existing anonymization techniques are inappropriate for situations where δ-presence is a good metric (specifically, where knowing an individual is in the database poses a privacy risk), and present algorithms for effectively anonymizing to meet it. Expand
  • 296
  • 12
  • PDF
A secure distributed framework for achieving k-anonymity
TLDR
This paper presents a two-party framework along with an application that generates k-anonymous data from two vertically partitioned sources without disclosing data from one site to the other. Expand
  • 205
  • 12
  • PDF