2017/11/20 by Ehsan Kazemi, Morteza Zadimoghaddam, Kazemi, Ehsan +3
Computer Science · #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques #Complexity and Algorithms in Graphs
paper · pdf · doi:10.48550/arxiv.1711.07112
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.