{"type":"ScholarlyArticle","title":"Enhancing Scalability of Metric Differential Privacy via Secret Dataset Partitioning and Benders Decomposition","authors":[{"name":"Chenxi Qiu","slug":"chenxi-qiu","given":"Chenxi","family":"Qiu","affiliation":"Department of Computer Science and Engineering , University of North Texas"}],"abstract":"Metric Differential Privacy (mDP) extends the concept of Differential Privacy (DP) to serve as a new paradigm of data perturbation. It is designed to protect secret data represented in general metric space, such as text data encoded as word embeddings or geo-location data on the road network or grid maps. To derive an optimal data perturbation mechanism under mDP, a widely used method is linear programming (LP), which, however, might suffer from a polynomial explosion of decision variables, rendering it impractical in large-scale mDP. \r\n\r\nIn this paper, our objective is to develop a new computation framework to enhance the scalability of the LP-based mDP. Considering the connections established by the mDP constraints among the secret records, we partition the original secret dataset into various subsets. Building upon the partition, we reformulate the LP problem for mDP and solve it via Benders Decomposition, which is composed of two stages: (1) a master program to manage the perturbation calculation across subsets, and (2) a set of subproblems, each managing the perturbation derivation within a subset. Our experimental results on multiple datasets, including geo-location data in the road network/grid maps, text data, and synthetic data, underscore our proposed mechanism’s superior scalability and efficiency.","doi":"10.24963/ijcai.2024/215","url":"https://ijcai.org/proceedings/2024/215","pdf":"https://ijcai.org/proceedings/2024/0215.pdf","markdown":"https://ijcai.org/proceedings/2024/215.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/215","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Constraint Satisfaction and Optimization","track":"Main Track","number":215,"pages":{"first":1944,"last":1952},"keywords":["Multidisciplinary Topics and Applications: MTA: Security and privacy","Constraint Satisfaction and Optimization: CSO: Constraint optimization problems","Constraint Satisfaction and Optimization: CSO: Constraint programming"],"videos":[],"references":[{"position":1,"raw":"[Andrés et al., 2013] Miguel E. 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