# A novel analysis of utility in privacy pipelines, using Kronecker products and quantitative information flow

arXiv: https://arxiv.org/abs/2308.11110 · HTML: https://mlsanity.com/paper/2308.11110/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2023.

## Built on by (most cited first, 1 of 1)

- [SoK: A Review of Differentially Private Linear Models For High-Dimensional Data](https://mlsanity.com/paper/2404.01141/) (2024)

## Abstract (arXiv)

We combine Kronecker products, and quantitative information flow, to give a novel formal analysis for the fine-grained verification of utility in complex privacy pipelines. The combination explains a surprising anomaly in the behaviour of utility of privacy-preserving pipelines -- that sometimes a reduction in privacy results also in a decrease in utility. We use the standard measure of utility for Bayesian analysis, introduced by Ghosh at al., to produce tractable and rigorous proofs of the fine-grained statistical behaviour leading to the anomaly. More generally, we offer the prospect of formal-analysis tools for utility that complement extant formal analyses of privacy. We demonstrate our results on a number of common privacy-preserving designs.