 For both models, our dependence on \epsilon shows, up to \log\frac{1}{\epsilon} factors, that there is no overhead over the standard insertion-only data stream model for any of these problems. } 
<meaning-changed>  For both models, our dependence on \epsilon shows, up to \log\frac{1}{\epsilon} factors, that there is no overhead over the standard insertion-only data stream model for any of these problems. } 
 For both models, our dependence on \epsilon shows, up to \log\frac{1}{\epsilon} factors, that there is no overhead over the standard insertion-only data stream model for any of these problems. } so-called flip number. However, the key technical innovation is that we apply the transformation to what we call a difference estimator for the streaming problem, rather than an estimator for the streaming problem itself. We then develop the first difference estimators for a wide range of problems. Our difference estimator methodology is not only applicable to the adversarially robust model, but to other streaming models where temporal properties of the data play a central role. (Abstract shortened to meet arXiv limit.)