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Shapley values of individual instances were recently proposed for the problem of data valuation. They were defined as the average marginal instance contributions to the performance of a given predictor. In this paper we propose to use Shapley values of individual instances as conformity scores. To compute these values efficiently and exactly we employ a standard algorithm based on nearest neighbor classification and propose a variant of this algorithm for clustered data. Both variants are used for computing Shapley conformity scores for inductive conformal predictors. The experiments show that the Shapley-value conformity scores result in smaller prediction sets for significance level epsilon <= 0:1 compared with those produced by standard conformity scores (i.e. similarity between true and predicted output values).