Quantifying the costs and benefits of privacy-preserving health data publishing

Rashid Hussain Khokhar, Rui CHEN, Benjamin C.M. Fung*, Siu Man Lui

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

39 Citations (Scopus)


Cost-benefit analysis is a prerequisite for making good business decisions. In the business environment, companies intend to make profit from maximizing information utility of published data while having an obligation to protect individual privacy. In this paper, we quantify the trade-off between privacy and data utility in health data publishing in terms of monetary value. We propose an analytical cost model that can help health information custodians (HICs) make better decisions about sharing person-specific health data with other parties. We examine relevant cost factors associated with the value of anonymized data and the possible damage cost due to potential privacy breaches. Our model guides an HIC to find the optimal value of publishing health data and could be utilized for both perturbative and non-perturbative anonymization techniques. We show that our approach can identify the optimal value for different privacy models, including K-anonymity, LKC-privacy, and {small element of} -differential privacy, under various anonymization algorithms and privacy parameters through extensive experiments on real-life data.

Original languageEnglish
Pages (from-to)107-121
Number of pages15
JournalJournal of Biomedical Informatics
Publication statusPublished - Aug 2014

Scopus Subject Areas

  • Computer Science Applications
  • Health Informatics

User-Defined Keywords

  • Cost model
  • Data utility
  • Health data
  • Monetary value
  • Privacy


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