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Efficient differentially private learning improves drug sensitivity prediction
2018
Biology Direct
Users of a personalised recommendation system face a dilemma: recommendations can be improved by learning from data, but only if the other users are willing to share their private information. Good personalised predictions are vitally important in precision medicine, but genomic information on which the predictions are based is also particularly sensitive, as it directly identifies the patients and hence cannot easily be anonymised. Differential privacy has emerged as a potentially promising
doi:10.1186/s13062-017-0203-4
pmid:29409513
pmcid:PMC5801888
fatcat:b6nvoxpjl5chdci55ug3sc3dt4