mlsanityData to 14 Oct 2026

Quantum Solutions to the Privacy vs. Utility Tradeoff

arXiv 2307.03118 ↗First posted 6 Jul 2023quant-ph cs.CR cs.LGcited by 1

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Abstract (arXiv)

In this work, we propose a novel architecture (and several variants thereof) based on quantum cryptographic primitives with provable privacy and security guarantees regarding membership inference attacks on generative models. Our architecture can be used on top of any existing classical or quantum generative models. We argue that the use of quantum gates associated with unitary operators provides inherent advantages compared to standard Differential Privacy based techniques for establishing guaranteed security from all polynomial-time adversaries.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 32 of its 43 references that are in the map. First posted on arXiv 6 Jul 2023.

Builds on

32 of 43 references.

References · 32

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  5. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  6. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
  7. 2022Generative Adversarial Networkscited by 5,627
  8. 2013Auto-Encoding Variational Bayescited by 4,053
  9. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  10. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  11. 2021Membership Inference Attacks From First Principlescited by 878
  12. 2023Extracting Training Data from Diffusion Modelscited by 732
20 more
  1. 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANscited by 516
  2. 2017Towards Principled Methods for Training Generative Adversarial Networkscited by 464
  3. 2022Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Modelscited by 424
  4. 2020Label-Only Membership Inference Attackscited by 388
  5. 2020Systematic Evaluation of Privacy Risks of Machine Learning Modelscited by 292
  6. 2023Understanding and Mitigating Copying in Diffusion Modelscited by 209
  7. 2023Are Diffusion Models Vulnerable to Membership Inference Attacks?cited by 136
  8. 2019Reconstruction and Membership Inference Attacks against Generative Modelscited by 122
  9. 2022Differentially Private Diffusion Modelscited by 119
  10. 2022Membership Inference Attacks by Exploiting Loss Trajectorycited by 101
  11. 2023Membership Inference Attacks against Diffusion Modelscited by 86
  12. 2023Membership Inference of Diffusion Modelscited by 46
  13. 2019Quantum Wasserstein Generative Adversarial Networkscited by 33
  14. 2023DP-LDMs: Differentially Private Latent Diffusion Modelscited by 29
  15. 2019(Pseudo) Random Quantum States with Binary Phasecited by 10
  16. 2023Pseudorandom unitaries are neither real nor sparse nor noise-robustcited by 5
  17. 2017Pseudorandom States, Non-Cloning Theorems and Quantum Moneycited by 3
  18. 2022Assessing Differentially Private Variational Autoencoders under Membership Inferencecited by 2
  19. 2023Quantum trapdoor functions from classical one-way functionscited by 1
  20. 2023Encryption with Quantum Public Keyscited by 1

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Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024Quantum Adversarial Machine Learning and Defense Strategies: Challenges and Opportunitiescited by 1

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  1. 2026Characterizing Privacy Risks of Quantum Machine Learning with Emergent Quantum-Native Access
  2. 2025Quantum Machine Unlearning: Foundations, Mechanisms, and Taxonomy
  3. 2026Equivariant Quantum Clustering with Differential Privacy: Parameter-Efficient Privacy-Preserving Analysis Across Heterogeneous Sensitive Datasets
  4. 2025Adversarial Threats in Quantum Machine Learning: A Survey of Attacks and Defenses
  5. 2024Federated learning with distributed fixed design quantum chips and quantum channels
  6. 2024Quantum-secure multiparty deep learning
  7. 2022Watermarking PRFs against Quantum Adversaries ↗
  8. 2026Quantum-Secure-By-Construction (QSC): A Paradigm Shift For Post-Quantum Agentic Intelligence