{"type":"ScholarlyArticle","title":"Differentially Private Partial Set Cover with Applications to Facility Location","authors":[{"name":"George Z. Li","slug":"george-z-li","given":"George Z.","family":"Li","affiliation":"University of Maryland"},{"name":"Dung Nguyen","slug":"dung-nguyen","given":"Dung","family":"Nguyen","affiliation":"Biocomplexity Institute and Initiative , and Department of Computer Science , University of Virginia"},{"name":"Anil Vullikanti","slug":"anil-vullikanti","given":"Anil","family":"Vullikanti","affiliation":"Biocomplexity Institute and Initiative , and Department of Computer Science , University of Virginia"}],"abstract":"Set Cover is a fundamental problem in combinatorial optimization which has been studied for many decades due to its various applications across multiple domains. In many of these domains, the input data consists of locations, relationships, and other sensitive information of individuals which may leaked due to the set cover output. Attempts have been made to design privacy-preserving algorithms to solve the Set Cover under privacy constraints. Under differential privacy, it has been proved that the Set Cover problem has strong impossibility results and no explicit forms of the output can be released to the public.\r\n\r\nIn this work, we observe that these hardness results dissolve when we turn to the Partial Set Cover problem, where we only need to cover a ρ ∈ (0,1) fraction of the elements. We show that this relaxation enables us to avoid the impossibility results, and give the first algorithm which outputs an explicit form of set cover with non-trivial utility guarantees under differential privacy. Using our algorithm as a subroutine, we design a differentially private bicriteria algorithm to solve a recently proposed facility location problem for vaccine distribution which generalizes the k-supplier with outliers. Our analysis shows that relaxing the covering requirement to serve only a ρ ∈ (0,1) fraction of the population/universe also allows us to circumvent the inherent hardness of k-supplier and give the first non-trivial guarantees.","doi":"10.24963/ijcai.2023/534","url":"https://ijcai.org/proceedings/2023/534","pdf":"https://ijcai.org/proceedings/2023/0534.pdf","markdown":"https://ijcai.org/proceedings/2023/534.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/534","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":534,"pages":{"first":4803,"last":4811},"keywords":["Multidisciplinary Topics and Applications: MDA: Security and privacy","Data Mining: DM: Privacy-preserving data mining"],"videos":[],"references":[{"position":1,"raw":"[Alon et al., 2003] Noga Alon, Baruch Awerbuch, Yossi Azar, Niv Buchbinder, and Joseph Naor. 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