We study the problem of differentially private constrained maximization of decomposable submodular functions. A submodular function is decomposable if it takes the form of a sum of submodular functions. The special case of maximizing a monotone, decomposable submodular function under cardinality constraints is known as the Combinatorial Public Projects (CPP) problem (Papadimitriou, Schapira, and Singer 2008). Previous work by Gupta et al. (2010) gave a differentially private algorithm for the CPP problem. We extend this work by designing differentially private algorithms for both monotone and nonmonotone decomposable submodular maximization under general matroid constraints, with competitive utility guarantees. We complement our theoretical bounds with experiments demonstrating improved empirical performance.
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This content will become publicly available on July 23, 2024
Streaming submodular maximization with differential privacy
In this work, we study the problem of privately maximizing a submodular function in the streaming setting. Extensive work has been done on privately maximizing submodular functions in the general case when the function depends upon the private data of individuals. However, when the size of the data stream drawn from the domain of the objective function is large or arrives very fast, one must privately optimize the objective within the constraints of the streaming setting. We establish fundamental differentially private baselines for this problem and then derive better tradeoffs between privacy and utility for the special case of decomposable submodular functions. A submodular function is decomposable when it can be written as a sum of submodular functions; this structure arises naturally when each summand function models the utility of an individual and the goal is to study the total utility of the whole population as in the wellknown Combinatorial Public Projects Problem. Finally, we complement our theoretical analysis with experimental corroboration.
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 Award ID(s):
 1750716
 NSFPAR ID:
 10493907
 Publisher / Repository:
 Proceedings of Machine Learning Research
 Date Published:
 Journal Name:
 Proceedings of Machine Learning Research
 Volume:
 202
 ISSN:
 26403498
 Page Range / eLocation ID:
 41164143
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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