# Detecting Violations of Differential Privacy for Quantum Algorithms

arXiv: https://arxiv.org/abs/2309.04819 · HTML: https://mlsanity.com/paper/2309.04819/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 58 references that are in the map.
- First posted on arXiv 9 Sep 2023.

## Builds on (most cited first, 18 of 18)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://mlsanity.com/paper/1905.07697/) (2019)
- [Quantum generative adversarial networks](https://mlsanity.com/paper/1804.08641/) (2018)
- [QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits](https://mlsanity.com/paper/2107.10845/) (2021)
- [Recurrent Quantum Neural Networks](https://mlsanity.com/paper/2006.14619/) (2020)
- [An Abstraction-Based Framework for Neural Network Verification](https://mlsanity.com/paper/1910.14574/) (2019)
- [Quantum noise protects quantum classifiers against adversaries](https://mlsanity.com/paper/2003.09416/) (2020)
- [Differential Privacy and Machine Learning: a Survey and Review](https://mlsanity.com/paper/1412.7584/) (2014)
- [Introduction to Neural Network Verification](https://mlsanity.com/paper/2109.10317/) (2021)
- [Detecting Violations of Differential Privacy](https://mlsanity.com/paper/1805.10277/) (2018)
- [Quantum Differential Privacy: An Information Theory Perspective](https://mlsanity.com/paper/2202.10717/) (2022)
- [Robustness Verification of Quantum Classifiers](https://mlsanity.com/paper/2008.07230/) (2020)
- [A unified view on differential privacy and robustness to adversarial examples](https://mlsanity.com/paper/1906.07982/) (2019)
- [Differential Privacy Amplification in Quantum and Quantum-inspired Algorithms](https://mlsanity.com/paper/2203.03604/) (2022)
- [Proving Differential Privacy via Probabilistic Couplings](https://mlsanity.com/paper/1601.05047/) (2016)
- [Proving differential privacy in Hoare logic](https://mlsanity.com/paper/1407.2988/) (2014)
- [Advanced Probabilistic Couplings for Differential Privacy](https://mlsanity.com/paper/1606.07143/) (2016)

## Built on by (most cited first, 3 of 3)

- [Entangled Threats: A Unified Kill Chain Model for Quantum Machine Learning Security](https://mlsanity.com/paper/2507.08623/) (2025)
- [Black-Box Auditing of Quantum Model: Lifted Differential Privacy with Quantum Canaries](https://mlsanity.com/paper/2512.14388/) (2025)
- [Optimal Guarantees for Auditing R'enyi Differentially Private Machine Learning](https://mlsanity.com/paper/2605.21938/) (2026)

## Abstract (arXiv)

Quantum algorithms for solving a wide range of practical problems have been proposed in the last ten years, such as data search and analysis, product recommendation, and credit scoring. The concern about privacy and other ethical issues in quantum computing naturally rises up. In this paper, we define a formal framework for detecting violations of differential privacy for quantum algorithms. A detection algorithm is developed to verify whether a (noisy) quantum algorithm is differentially private and automatically generate bugging information when the violation of differential privacy is reported. The information consists of a pair of quantum states that violate the privacy, to illustrate the cause of the violation. Our algorithm is equipped with Tensor Networks, a highly efficient data structure, and executed both on TensorFlow Quantum and TorchQuantum which are the quantum extensions of famous machine learning platforms -- TensorFlow and PyTorch, respectively. The effectiveness and efficiency of our algorithm are confirmed by the experimental results of almost all types of quantum algorithms already implemented on realistic quantum computers, including quantum supremacy algorithms (beyond the capability of classical algorithms), quantum machine learning models, quantum approximate optimization algorithms, and variational quantum eigensolvers with up to 21 quantum bits.