Adaptive Differential Privacy of Character and Its Application for Genome Data Sharing

Hai, Liu; Wu, Zhenqiang; Peng, Changgen; Lei, Xiujuan; Tian, Feng; Lu, Laifeng

doi:10.1109/nana.2019.00080

Cited by 1 publication

(2 citation statements)

References 18 publications

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“…However, genome data research with differential privacy only achieves the privacy-utility tradeoff. Therefore, Liu et al [16] proposed adaptive differential privacy of categorical data to achieve desired privacy preserving and desired data utility of genome data sharing. However, adaptive differential privacy of categorical data can not be directly used for aggregating multiparty genome data to achieve privacy-utility equilibrium.…”

Section: B Genome Data Research With Differential Privacymentioning

confidence: 99%

“…Differential privacy is a strict privacy preserving framework without considering all the background knowledge except a single record [4]. Therefore, differential privacy has been used to to protect sensitive information of the genome data [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16] Manuscript received November 10, 2021; revised December 30, 2021. Corresponding author: Changgen Peng (email: peng stud@163.com).…”

Section: Introductionmentioning

confidence: 99%

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Privacy-Utility Equilibrium Protocol for Federated Aggregating Multiparty Genome Data

2021

J-NaNA

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Cloud server aggregates a large amount of genome data from multi genome donors to facilitate scientific research. However, the untrusted cloud server is prone to violate privacy of aggregating genome data. Thus, each genome donor can randomly perturb her genome data using differential privacy mechanism before aggregating. But this is easy to lead to utility disaster of aggregating genome data due to the different privacy preferences of each genome donor, and privacy leakage of aggregating genome data because of the kinship between genome donors. The key challenge here is to achieve an equilibrium between privacy preserving and data utility of aggregating multiparty genome data. To this end, we proposed federated aggregation protocol of multiparty genome data (MGD-FAP) with privacy-utility equilibrium for guaranteeing desired privacy protection and desired data utility. First, we regarded the privacy budget and the accuracy as the desired privacy-utility metrics of genome data respectively. Second, we constructed the federated aggregation model of multiparty genome data by combining random perturbation method of genome data guaranteeing desired data utility with federated comparing update method of local privacy budget achieving desired privacy preserving. Third, we presented the MGD-FAP maintaining privacy-utility equilibrium under the federated aggregation model of multiparty genome data. Finally, our theoretical and experimental analysis showed that MGD-FAP can maintain privacy-utility equilibrium. The MGD-FAP is practical and feasible to ensure the privacy-utility equilibrium of cloud server aggregating multiparty genome data.

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Section: B Genome Data Research With Differential Privacymentioning

confidence: 99%