Abstract
We design a scalable algorithm to privately generate location heatmaps over decentralized data from millions of user devices. It aims to ensure differential privacy before data becomes visible to a service provider while maintaining high data accuracy and minimizing resource consumption on users’ devices. To achieve this, we revisit distributed differential privacy based on recent results in secure multiparty computation, and we design a scalable and adaptive distributed differential privacy approach for location analytics. Evaluation on public location datasets shows that this approach successfully generates metropolitan-scale heatmaps from millions of user samples with a worstcase client communication overhead that is significantly smaller than existing state-of-the-art private protocols of similar accuracy.
Publisher
Privacy Enhancing Technologies Symposium Advisory Board
Cited by
4 articles.
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1. Federated Submodular Maximization With Differential Privacy;IEEE Internet of Things Journal;2024-01-15
2. Federated computation: a survey of concepts and challenges;Distributed and Parallel Databases;2023-11-23
3. Building Quadtrees for Spatial Data Under Local Differential Privacy;Data and Applications Security and Privacy XXXVII;2023
4. FedWalk;Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2022-08-14