A Practical Methodology for Anonymization of Structured Health Data

EasyChair Preprint 1667

Abstract

Hospitals, as data custodians, have the need to share a version of the data in hand with external research institutes for analysis purposes. For preserving the privacy of the patients, anonymization methods are employed to produce a modified version of data for publishing; these methodologies shall not reveal the patient’s information while maintaining the utility of data. In this article, we propose a practical methodology for anonymization of structured health data based on cryptographic algorithms, which preserves the privacy by construction. Our initial experimental results indicate that the methodology might outperform the existing solutions by retaining the utility of data.

Keyphrases: Anonymization, Cryptography, Data Mining, privacy-preserving data sharing, structured health data

@booklet{EasyChair:1667,
  author    = {Amin Aminifar and Yngve Lamo and Ka I Pun and Fazle Rabbi},
  title     = {A Practical Methodology for Anonymization of Structured Health Data},
  howpublished = {EasyChair Preprint 1667},
  year      = {EasyChair, 2019}}