In longitudinal datasets, it is usually unrealistic that an adversary would know the value of every quasi-identifier. De-identifying a dataset under this assumption results in high levels of generalization and suppression as every patient is unique. Adversary power gives an upper bound on the number of values an adversary knows about a patient. Considering all subsets of quasi-identifiers with the size of the adversary power is computationally infeasible. A method is provided to assess re-identification risk by determining a representative risk which can be used as a proxy for the overall risk measurement and enable suppression of identifiable quasi-identifiers.