# Deletion-Robust Submodular Maximization at Scale

arXiv: https://arxiv.org/abs/1711.07112 · HTML: https://mlsanity.com/paper/1711.07112/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2017.

## Built on by (most cited first, 2 of 2)

- [Deletion Robust Submodular Maximization over Matroids](https://mlsanity.com/paper/2201.13128/) (2022)
- [Deletion Robust Non-Monotone Submodular Maximization over Matroids](https://mlsanity.com/paper/2208.07582/) (2022)

## Abstract (arXiv)

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.