Publishing differentially private medical events data

Sigal Shaked, Lior Rokach

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Sequential data has been widely collected in the past few years; in the public health domain it appears as collections of medical events such as lab results, electronic chart records, or hospitalization transactions. Publicly available sequential datasets for research purposes promises new insights, such as understanding patient types, and recognizing emerging diseases. Unfortunately, the publication of sequential data presents a significant threat to users’ privacy. Since data owners prefer to avoid such risks, much of the collected data is currently unavailable to researchers. Existing anonymization techniques that aim at preserving sequential patterns lack two important features: handling long sequences and preserving occurrence times. In this paper, we address this challenge by employing an ensemble of Markovian models trained based on the source data. The ensemble takes several optional periodicity levels into consideration. Each model captures transitions between times and states according to shorter parts of the sequence, which is eventually reconstructed. Anonymity is provided by utilizing only elements of the model that guarantee differential privacy. Furthermore, we develop a solution for generating differentially private sequential data, which will bring us one step closer to publicly available medical datasets via sequential data. We applied this method to two real medical events datasets and received some encouraging results, demonstrating that the proposed method can be used to publish high quality anonymized data.

Original languageEnglish
Title of host publicationAvailability, Reliability, and Security in Information Systems - IFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference, CD-ARES 2016 and Workshop on Privacy Aware Machine Learning for Health Data Science, PAML 2016 Salzburg, Proceedings
EditorsPeter Kieseberg, Edgar Weippl, Andreas Holzinger, Francesco Buccafurri, A. Min Tjoa
PublisherSpringer Verlag
Pages219-235
Number of pages17
ISBN (Print)9783319455068
DOIs
StatePublished - 23 Aug 2016
EventIFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference, CD-ARES 2016 and Workshop on Privacy Aware Machine Learning for Health Data Science, PAML 2016 - Salzburg, Austria
Duration: 31 Aug 20162 Sep 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9817 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceIFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference, CD-ARES 2016 and Workshop on Privacy Aware Machine Learning for Health Data Science, PAML 2016
Country/TerritoryAustria
CitySalzburg
Period31/08/162/09/16

Keywords

  • Clustering
  • Data synthetization
  • Differential privacy
  • Markov model
  • Medical events
  • Privacy preserving data publishing
  • Sequential patterns

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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