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This content will become publicly available on July 23, 2024

Title: 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 trade-offs 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 well-known Combinatorial Public Projects Problem. Finally, we complement our theoretical analysis with experimental corroboration.  more » « less
Award ID(s):
1750716
NSF-PAR ID:
10493907
Author(s) / Creator(s):
; ;
Publisher / Repository:
Proceedings of Machine Learning Research
Date Published:
Journal Name:
Proceedings of Machine Learning Research
Volume:
202
ISSN:
2640-3498
Page Range / eLocation ID:
4116-4143
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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