Feasibility of Privacy-Preserving Entity Resolution on Confidential Healthcare Datasets Using Homomorphic Encryption
<|reference_start|>Feasibility of Privacy-Preserving Entity Resolution on Confidential Healthcare Datasets Using Homomorphic Encryption: Patient datasets contain confidential information which is protected by laws and regulations such as HIPAA and GDPR. Ensuring comprehensive patient information necessitates privacy-preserving entity resolution (PPER), which identifies identical patient entities across multiple databases from different healthcare organizations while maintaining data privacy. Existing methods often lack cryptographic security or are computationally impractical for real-world datasets. We introduce a PPER pipeline based on AMPPERE, a secure abstract computation model utilizing cryptographic tools like homomorphic encryption. Our tailored approach incorporates extensive parallelization techniques and optimal parameters specifically for patient datasets. Experimental results demonstrate the proposed method's effectiveness in terms of accuracy and efficiency compared to various baselines.<|reference_end|>
@article{yao2024feasibility,
    title={Feasibility of Privacy-Preserving Entity Resolution on Confidential
  Healthcare Datasets Using Homomorphic Encryption},
    author={Yixiang Yao, Joseph Cecil, Praveen Angyan, Neil Bahroos, Srivatsan
  Ravi},
    journal={arXiv preprint arXiv:2405.18430},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18430},
    primaryClass={cs.CE}
}