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Aforementioned works are based on private datasets and/or different experimental protocols.
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The first approach is based on individual datasets.
Medication costs were based on private quotes for current drug price lists (i.e. Thériaque) [ 29].
Additional functions allow users to generate specific networks based on private databases.
Techniques based on wavelets report quantified results even in the earlier studies like [57, 58] where accuracies of 74.4 and 85% are reported (respectively) on private datasets.
On private datasets, classification accuracies of as high as 100% have also been reported [33, 35].
Techniques proposed in most of the aforementioned studies have been evaluated on private datasets.
Logistic regression models built with our new algorithm based on both private and public datasets demonstrate better utility than models that trained on private or public datasets alone without sacrificing the rigorous privacy guarantee.
Our analysis was based on one protein dataset [ 4] and two microarray datasets [ 5, 6].
Ji et al. (UC San Diego, USA) proposed a logistic regression-based method for differential privacy against attackers who have auxiliary information based on both private and public datasets [ 15].
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