Privacy, Prediction, and Allocation

TL;DR

This paper explores the tradeoffs between privacy, efficiency, and targeting precision in resource allocation systems using differential privacy, analyzing private variants of allocation strategies.

Contribution

It synthesizes private optimization with aid allocation models, providing bounds on privacy-utility tradeoffs in various settings.

Findings

Derived bounds on privacy-utility tradeoffs in allocation strategies.

Analyzed private variants of both individual and unit-level allocation.

Provided insights into the interplay of privacy and targeting in resource allocation.

Abstract

Algorithmic predictions are increasingly used to inform the allocation of scarce resources. The promise of these methods is that, through machine learning, they can better identify the people who would benefit most from interventions. Recently, however, several works have called this assumption into question by demonstrating the existence of settings where simple, unit-level allocation strategies can meet or even exceed the performance of those based on individual-level targeting. Separately, other works have objected to individual-level targeting on privacy grounds, leading to an unusual situation where a single solution, unit-level targeting, is recommended for reasons of both privacy and utility. Motivated by the desire to fully understand the interplay of privacy and targeting levels, we initiate the study of aid allocation systems that satisfy differential privacy, synthesizing existing works on private optimization with the economic models of aid allocation used in the non-private literature. To this end, we investigate private variants of both individual and unit-level allocation strategies in both stochastic and distribution-free settings under a range of constraints on data availability. Through this analysis, we provide clean, interpretable bounds characterizing the tradeoffs between privacy, efficiency, and targeting precision in allocation.