SDVisor: Secure Debug Enclave with Hypervisor

Hong, Zhen; Li, Zinan; Xia, Yubin

doi:10.1109/sose.2019.00036

Cited by 2 publications

(1 citation statement)

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“…fully homomorphic encryption), and synthetic data publishing. First, secure data enclave environments 12,13 offer a platform for researchers to analyze sensitive data without compromising risks for misuse and other violations. Many health information storage solutions, for example, EHRs, rely on technology that provides managed data access for research in safe and controlled environments.…”

Section: Introductionmentioning

confidence: 99%

DataSifter II: Partially synthetic data sharing of sensitive information containing time-varying correlated observations

Zhou

Wang

Marino

et al. 2022

Journal of Algorithms & Computational Technology

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There is a significant public demand for rapid data-driven scientific investigations using aggregated sensitive information. However, many technical challenges and regulatory policies hinder efficient data sharing. In this study, we describe a partially synthetic data generation technique for creating anonymized data archives whose joint distributions closely resemble those of the original (sensitive) data. Specifically, we introduce the DataSifter technique for time-varying correlated data (DataSifter II), which relies on an iterative model-based imputation using generalized linear mixed model and random effects-expectation maximization tree. DataSifter II can be used to generate synthetic repeated measures data for testing and validating new analytical techniques. Compared to the multiple imputation method, DataSifter II application on simulated and real clinical data demonstrates that the new method provides extensive reduction of re-identification risk (data privacy) while preserving the analytical value (data utility) in the obfuscated data. The performance of the DataSifter II on a simulation involving 20% artificially missingness in the data, shows at least 80% reduction of the disclosure risk, compared to the multiple imputation method, without a substantial impact on the data analytical value. In a separate clinical data (Medical Information Mart for Intensive Care III) validation, a model-based statistical inference drawn from the original data agrees with an analogous analytical inference obtained using the DataSifter II obfuscated ( sifted) data. For large time-varying datasets containing sensitive information, the proposed technique provides an automated tool for alleviating the barriers of data sharing and facilitating effective, advanced, and collaborative analytics.

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Section: Introductionmentioning

confidence: 99%