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Title: SDP Methods for Sensitivity-Constrained Privacy Funnel and Information Bottleneck Problems
We generalize the information bottleneck (IB) and privacy funnel (PF) problems by introducing the notion of a sensitive attribute, which arises in a growing number of applications. In this generalization, we seek to construct representations of observations that are maximally (or minimally) informative about a target variable, while also satisfying constraints with respect to a variable corresponding to the sensitive attribute. In the Gaussian and discrete settings, we show that by suitably approximating the Kullback-Liebler (KL) divergence defining traditional Shannon mutual information, the generalized IB and PF problems can be formulated as semi-definite programs (SDPs), and thus efficiently solved, which is important in applications of high-dimensional inference. We validate our algorithms on synthetic data and demonstrate their use in imposing fairness in machine learning on real data as an illustrative application.
Authors:
; ;
Award ID(s):
1717610
Publication Date:
NSF-PAR ID:
10378617
Journal Name:
IEEE International Symposium on Information Theory
Page Range or eLocation-ID:
49 - 54
Sponsoring Org:
National Science Foundation
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