Sharing Privacy Protected and Statistically Sound Clinical Research Data Using Outsourced Data Storage

Geontae Noh, Ji Young Chun and Ik Rae Jeong

Journal of Applied Mathematics, 2014, vol. 2014, issue 1

Abstract: It is critical to scientific progress to share clinical research data stored in outsourced generally available cloud computing services. Researchers are able to obtain valuable information that they would not otherwise be able to access; however, privacy concerns arise when sharing clinical data in these outsourced publicly available data storage services. HIPAA requires researchers to deidentify private information when disclosing clinical data for research purposes and describes two available methods for doing so. Unfortunately, both techniques degrade statistical accuracy. Therefore, the need to protect privacy presents a significant problem for data sharing between hospitals and researchers. In this paper, we propose a controlled secure aggregation protocol to secure both privacy and accuracy when researchers outsource their clinical research data for sharing. Since clinical data must remain private beyond a patient’s lifetime, we take advantage of lattice‐based homomorphic encryption to guarantee long‐term security against quantum computing attacks. Using lattice‐based homomorphic encryption, we design an aggregation protocol that aggregates outsourced ciphertexts under distinct public keys. It enables researchers to get aggregated results from outsourced ciphertexts of distinct researchers. To the best of our knowledge, our protocol is the first aggregation protocol which can aggregate ciphertexts which are encrypted with distinct public keys.

Date: 2014
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https://doi.org/10.1155/2014/381361

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jnljam:v:2014:y:2014:i:1:n:381361

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