A digital goods auction is a type of auction where potential buyers bid the maximal price that they are willing to pay for a certain item, which a seller can produce at a negligible cost and in unlimited quantity. To maximise her benefits, the aim for the seller is to find the optimal sales price, which every buyer whose bid is not lower will pay. For fairness and privacy purposes, buyers may be concerned about protecting the confidentiality of their bids. Secure Multi-Party Computation is a domain of Cryptography that would allow the seller to compute the optimal sales price while guaranteeing that the bids remain secret. Paradoxically, as a function of the buyers' bids, the sales price inevitably reveals some private information. Generic frameworks and entropy-based techniques based on Quantitative Information Flow have been developed in order to quantify and restrict those leakages. Due to their combinatorial nature, these techniques do not scale to large input spaces. In this work, we aim at scaling those privacy analyses to large input spaces in the particular case of digital goods auctions. We derive closed-form formulas for the posterior min-entropy of private inputs in two and three-party auctions, which enables us to effectively quantify the information leaks for arbitrarily large input spaces. We also provide supportive experimental evidence that enables us to formulate a conjecture that would allow us to extend our results to any number of parties.