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• W2769083670 abstract "Can we efficiently extract useful information from a large user-generated dataset while protecting the privacy of the users and/or ensuring fairness in representation. We cast this problem as an instance of a deletion-robust submodular maximization where part of the data may be deleted due to privacy concerns or fairness criteria. We propose the first memory-efficient centralized, streaming, and distributed methods with constant-factor approximation guarantees against any number of adversarial deletions. We extensively evaluate the performance of our algorithms against prior state-of-the-art on real-world applications, including (i) Uber-pick up locations with location privacy constraints; (ii) feature selection with fairness constraints for income prediction and crime rate prediction; and (iii) robust to deletion summarization of census data, consisting of 2,458,285 feature vectors." @default.
• W2769083670 created "2017-12-04" @default.
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• W2769083670 date "2017-11-20" @default.
• W2769083670 modified "2023-09-27" @default.
• W2769083670 title "Deletion-Robust Submodular Maximization at Scale." @default.
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