# Differential Privacy as a Causal Property

arXiv: https://arxiv.org/abs/1710.05899 · HTML: https://mlsanity.com/paper/1710.05899/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2017.

## Built on by (most cited first, 4 of 4)

- [Rethinking Privacy in Machine Learning Pipelines from an Information Flow Control Perspective](https://mlsanity.com/paper/2311.15792/) (2023)
- [Algorithms with More Granular Differential Privacy Guarantees](https://mlsanity.com/paper/2209.04053/) (2022)
- [Two Views of Constrained Differential Privacy: Belief Revision and Update](https://mlsanity.com/paper/2303.00228/) (2023)
- [A Shuffling Framework for Local Differential Privacy](https://mlsanity.com/paper/2106.06603/) (2021)

## Abstract (arXiv)

We present associative and causal views of differential privacy. Under the associative view, the possibility of dependencies between data points precludes a simple statement of differential privacy's guarantee as conditioning upon a single changed data point. However, we show that a simple characterization of differential privacy as limiting the effect of a single data point does exist under the causal view, without independence assumptions about data points. We believe this characterization resolves disagreement and confusion in prior work about the consequences of differential privacy. The associative view needing assumptions boils down to the contrapositive of the maxim that correlation doesn't imply causation: differential privacy ensuring a lack of (strong) causation does not imply a lack of (strong) association. Our characterization also opens up the possibility of applying results from statistics, experimental design, and science about causation while studying differential privacy.