We consider the problem of local privacy where actions are subsets of a ground multiset and expectation rewards are modeled by a [Formula: see text]decomposable monotone submodular function. For the DR-submodular maximization problem under a polymatroid constraint, Soma and Yoshida [26] provide a continuous greedy algorithm for no-privacy setting. In this paper, we obtain the first differentially private algorithm for DR-submodular maximization subject to a polymatroid constraint. Our algorithm achieves a [Formula: see text]approximation with a little loss and runs in [Formula: see text][Formula: see text] times where [Formula: see text] is the rank of the base polymatroid and [Formula: see text] is the size of ground set. Along the way, we analyze the utility and privacy of our algorithm. A concrete experiment to simulate the privacy Uber pickups location problem is provided, and our algorithm performs well within the agreed range.

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