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Model pruning has been proposed as a technique for reducing the size and complexity of Federated learning (FL) models. By making local models coarser, pruning is intuitively expected to improve protection against privacy attacks. However, the level of this expected privacy protection has not been previously characterized, or optimized jointly with utility. In this paper, we first characterize the privacy offered by pruning. We establish information-theoretic upper bounds on the information leakage from pruned FL and we experimentally validate them under state-of-the-art privacy attacks across different FL pruning schemes. Second, we introducePriPrune– a privacy-aware algorithm for pruning in FL.PriPruneuses defense pruning masks, which can be applied locally afteranypruning algorithm, and adapts the defense pruning rate to jointly optimize privacy and accuracy. Another key idea in the design ofPriPruneisPseudo-Pruning: it undergoes defense pruning within the local model and only sends the pruned model to the server; while the weights pruned out by defense mask are withheld locally for future local training rather than being removed. We show thatPriPrunesignificantly improves the privacy-accuracy tradeoff compared to state-of-the-art pruned FL schemes. For example, on the FEMNIST dataset,PriPruneimproves the privacy ofPruneFLby 45.5% without reducing accuracy.