(ε, k)-Randomized Anonymization: ε-Differentially Private Data Sharing with k-Anonymity

Akito Yamamoto, Eizen Kimura, Tetsuo Shibuya

2023

Abstract

As the amount of biomedical and healthcare data increases, data mining for medicine becomes more and more important for health improvement. At the same time, privacy concerns in data utilization have also been growing. The key concepts for privacy protection are k-anonymity and differential privacy, but k-anonymity alone cannot protect personal presence information, and differential privacy alone would leak the identity. To promote data sharing throughout the world, universal methods to release the entire data while satisfying both concepts are required, but such a method does not yet exist. Therefore, we propose a novel privacy-preserving method, (ε, k)-Randomized Anonymization. In this paper, we first present two methods that compose the Randomized Anonymization method. They perform k-anonymization and randomized response in sequence and have adequate randomness and high privacy guarantees, respectively. Then, we show the algorithm for (ε, k)-Randomized Anonymization, which can provide highly accurate outputs with both k-anonymity and differential privacy. In addition, we describe the analysis procedures for each method using an inverse matrix and expectation-maximization (EM) algorithm. In the experiments, we used real data to evaluate our methods’ anonymity, privacy level, and accuracy. Furthermore, we show several examples of analysis results to demonstrate high utility of the proposed methods.

Paper Citation

in Harvard Style

Yamamoto A., Kimura E. and Shibuya T. (2023). (ε, k)-Randomized Anonymization: ε-Differentially Private Data Sharing with k-Anonymity. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF; ISBN 978-989-758-631-6, SciTePress, pages 287-297. DOI: 10.5220/0011665600003414

in Bibtex Style

@conference{healthinf23,
author={Akito Yamamoto and Eizen Kimura and Tetsuo Shibuya},
title={(ε, k)-Randomized Anonymization: ε-Differentially Private Data Sharing with k-Anonymity},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF},
year={2023},
pages={287-297},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011665600003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF
TI - (ε, k)-Randomized Anonymization: ε-Differentially Private Data Sharing with k-Anonymity
SN - 978-989-758-631-6
AU - Yamamoto A.
AU - Kimura E.
AU - Shibuya T.
PY - 2023
SP - 287
EP - 297
DO - 10.5220/0011665600003414
PB - SciTePress