We consider linear regression in the high-dimensional regime where the number of parameters exceeds the number of samples (p > n) and assume that the high-dimensional parameters vector is sparse. We develop a framework for testing general hypotheses regarding the model parameters. Our framework encompasses testing whether the parameter lies in a convex cone, testing the signal strength, and testing arbitrary functionals of the parameter. We show that the proposed procedure controls the false positive rate and also analyze the power of the procedure. Our numerical experiments confirm our theoretical findings and demonstrate that we control false positive rate near the nominal level, and have high power. By duality between hypotheses testing and confidence intervals, the proposed framework can be used to obtain valid confidence intervals for various functionals of the model parameters. For linear functionals, the length of confidence intervals is shown to be minimax rate optimal. [This talks is based on a joint work with Jason Lee.]