2019 International Conference on Networking and Network Applications (NaNA) 2019
DOI: 10.1109/nana.2019.00040
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Cited by 4 publications
(6 citation statements)
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“…These algorithms decide when to answer the query correctly/incorrectly according to specific conditions in the bias variable so that it gets harder for the attacker to succeed. Some of the efforts in the second category addressed the privacy concerns in GWAS by introducing differentially private logistic regression to identify associations between SNPs and diseases 80 or associations among multiple SNPs. 81 Honkela et al 82 improve drug sensitivity prediction by effectively employing differential privacy for Bayesian linear regression.…”
Section: Differential Privacymentioning
confidence: 99%
See 2 more Smart Citations
“…These algorithms decide when to answer the query correctly/incorrectly according to specific conditions in the bias variable so that it gets harder for the attacker to succeed. Some of the efforts in the second category addressed the privacy concerns in GWAS by introducing differentially private logistic regression to identify associations between SNPs and diseases 80 or associations among multiple SNPs. 81 Honkela et al 82 improve drug sensitivity prediction by effectively employing differential privacy for Bayesian linear regression.…”
Section: Differential Privacymentioning
confidence: 99%
“…Some of the efforts in the second category addressed the privacy concerns in GWAS by introducing differentially private logistic regression to identify associations between SNPs and diseases 80 or associations among multiple SNPs. 81 Honkela et al 82 improve drug sensitivity prediction by effectively employing differential privacy for Bayesian linear regression.…”
Section: Differential Privacymentioning
confidence: 99%
See 1 more Smart Citation
“…It has become standard in data protection and has been effectively deployed by Google [70] and Apple [71] as well as agencies such as the United States Census Bureau. Furthermore, it has drawn the attention of researchers in privacy-sensitive fields such as biomedicine and healthcare [66,[72][73][74][75][76][77][78][79][80][81][82][83][84][85][86]. Differential privacy ensures that the model we train does not overfit the sensitive data of a particular user.…”
Section: Differential Privacymentioning
confidence: 99%
“…The analysts can retrieve statistical properties such as the correlation between SNPs and get an almost accurate answer while the GWAS dataset is protected against privacy risks. Some of the efforts in the second category addressed the privacy concerns in GWAS data analysis by introducing differentially private logistic regression to identify associations between SNPs and diseases [81] or associations among multiple SNPs [79]. Honkela et al [80] improve drug sensitivity prediction by effectively employing differential privacy for Bayesian linear regression.…”
Section: Differential Privacymentioning
confidence: 99%