mlsanityData to 14 Oct 2026

Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment

arXiv 2304.06929 ↗First posted 14 Apr 2023cs.CRcited by 27

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Abstract (arXiv)

In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP), with a focus of advancing DP's deployment in real-world applications. Key points and high-level contents of the article were originated from the discussions from "Differential Privacy (DP): Challenges Towards the Next Frontier," a workshop held in July 2022 with experts from industry, academia, and the public sector seeking answers to broad questions pertaining to privacy and its implications in the design of industry-grade systems. This article aims to provide a reference point for the algorithmic and design decisions within the realm of privacy, highlighting important challenges and potential research directions. Covering a wide spectrum of topics, this article delves into the infrastructure needs for designing private systems, methods for achieving better privacy/utility trade-offs, performing privacy attacks and auditing, as well as communicating privacy with broader audiences and stakeholders.

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Apr 2023.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 6; 2024: 8; 2025: 7; 2026: 6.

27 papers.

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  1. 2023Detecting Pretraining Data from Large Language Modelscited by 387
  2. 2024Do Membership Inference Attacks Work on Large Language Models?cited by 195
  3. 2023Privacy Implications of Retrieval-Based Language Modelscited by 32
  4. 2024On the Challenges and Opportunities in Generative AIcited by 31
  5. 2024AI Risk Management Should Incorporate Both Safety and Securitycited by 17
  6. 2024Differentially Private Tabular Data Synthesis using Large Language Modelscited by 16
  7. 2024Do Parameters Reveal More than Loss for Membership Inference?cited by 10
  8. 2024Private Synthetic Text Generation with Diffusion Modelscited by 10
  9. 2025InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacycited by 10
  10. 2024A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theoremcited by 8
  11. 2023A unifying framework for differentially private quantum algorithmscited by 7
  12. 2025Machine Learners Should Acknowledge the Legal Implications of Large Language Models as Personal Datacited by 5
15 more
  1. 2025Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacycited by 4
  2. 2025ContextLeak: Auditing Leakage in Private In-Context Learning Methodscited by 4
  3. 2023Private Matrix Factorization with Public Item Featurescited by 3
  4. 2024Differentially Private Learning Needs Better Model Initialization and Self-Distillationcited by 3
  5. 2025On the Gradient Complexity of Private Optimization with Private Oraclescited by 2
  6. 2023On the Challenges of Deploying Privacy-Preserving Synthetic Data in the Enterprisecited by 1
  7. 2023RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulationcited by 1
  8. 2025Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AIcited by 1
  9. 2026Towards Provably Unbiased LLM Judges via Bias-Bounded Evaluationcited by 1
  10. 2025Infinitely divisible privacy and beyond I: resolution of the s2=2k conjecturecited by 0
  11. 2026Sequential Auditing for f-Differential Privacycited by 0
  12. 2026TADP-RME: A Trust-Adaptive Differential Privacy Framework for Enhancing Reliability of Data-Driven Systemscited by 0
  13. 2026MC-PDD: Masked Corpus-Level Pretraining Data Detection for Black-Box Large Language Modelscited by 0
  14. 2026The Privacy-Hallucination Tradeoff in Differentially Private Language Modelscited by 0
  15. 2026Can R'enyi Accountants Beat Lossless Accountants for DP-SGD?cited by 0