# Linear Program Reconstruction in Practice

arXiv: https://arxiv.org/abs/1810.05692 · HTML: https://mlsanity.com/paper/1810.05692/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2018.

## Built on by (most cited first, 14 of 14)

- [Reconstructing Training Data with Informed Adversaries](https://mlsanity.com/paper/2201.04845/) (2022)
- [A Linear Reconstruction Approach for Attribute Inference Attacks against Synthetic Data](https://mlsanity.com/paper/2301.10053/) (2023)
- [Confidence-Ranked Reconstruction of Census Microdata from Published Statistics](https://mlsanity.com/paper/2211.03128/) (2022)
- [The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasets](https://mlsanity.com/paper/2312.05114/) (2023)
- [A Review of Anonymization for Healthcare Data](https://mlsanity.com/paper/2104.06523/) (2021)
- [Exploiting Fairness to Enhance Sensitive Attributes Reconstruction](https://mlsanity.com/paper/2209.01215/) (2022)
- [Control, Confidentiality, and the Right to be Forgotten](https://mlsanity.com/paper/2210.07876/) (2022)
- [Probabilistic Dataset Reconstruction from Interpretable Models](https://mlsanity.com/paper/2308.15099/) (2023)
- [QuerySnout: Automating the Discovery of Attribute Inference Attacks against Query-Based Systems](https://mlsanity.com/paper/2211.05249/) (2022)
- [Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network Approach](https://mlsanity.com/paper/2309.11515/) (2023)
- [Federated Learning is Better with Non-Homomorphic Encryption](https://mlsanity.com/paper/2312.02074/) (2023)
- [A Unified Framework for Adversary-Aware Differential Privacy Bounds](https://mlsanity.com/paper/2507.08158/) (2025)
- [DeSIA: Attribute Inference Attacks Against Limited Fixed Aggregate Statistics](https://mlsanity.com/paper/2504.18497/) (2025)
- [Blind Targeting: Personalization under Third-Party Privacy Constraints](https://mlsanity.com/paper/2507.05175/) (2025)

## Abstract (arXiv)

We briefly report on a successful linear program reconstruction attack performed on a production statistical queries system and using a real dataset. The attack was deployed in test environment in the course of the Aircloak Challenge bug bounty program and is based on the reconstruction algorithm of Dwork, McSherry, and Talwar. We empirically evaluate the effectiveness of the algorithm and a related algorithm by Dinur and Nissim with various dataset sizes, error rates, and numbers of queries in a Gaussian noise setting.