De-Health: All your online health information are belong to us

Shouling Ji, Qinchen Gu, Haiqin Weng, Qianjun Liu, Pan Zhou, Jing Chen, Zhao Li, Raheem Beyah, Ting Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we study the privacy of online health data. We present a novel online health data De-Anonymization (DA) framework, named De-Health. Leveraging two real world online health datasets WebMD and HealthBoards, we validate the DA efficacy of De-Health. We also present a linkage attack framework which can link online health/medical information to real world people. Through a proof-of-concept attack, we link 347 out of 2805 WebMD users to real world people, and find the full names, medical/health information, birthdates, phone numbers, and other sensitive information for most of the re-identified users. This clearly illustrates the fragility of the privacy of those who use online health forums.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020
PublisherIEEE Computer Society
Pages1609-1620
Number of pages12
ISBN (Electronic)9781728129037
DOIs
StatePublished - Apr 2020
Event36th IEEE International Conference on Data Engineering, ICDE 2020 - Dallas, United States
Duration: Apr 20 2020Apr 24 2020

Publication series

NameProceedings - International Conference on Data Engineering
Volume2020-April
ISSN (Print)1084-4627

Conference

Conference36th IEEE International Conference on Data Engineering, ICDE 2020
CountryUnited States
CityDallas
Period4/20/204/24/20

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems

Fingerprint Dive into the research topics of 'De-Health: All your online health information are belong to us'. Together they form a unique fingerprint.

Cite this