  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.
