mlsanityData to 14 Oct 2026

A bounded-noise mechanism for differential privacy

arXiv 2012.03817 ↗First posted 7 Dec 2020cs.DS cs.CR cs.LGcited by 15

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Abstract (arXiv)

We present an asymptotically optimal (ε,δ) differentially private mechanism for answering multiple, adaptively asked, Delta-sensitive queries, settling the conjecture of Steinke and Ullman [2020]. Our algorithm has a significant advantage that it adds independent bounded noise to each query, thus providing an absolute error bound. Additionally, we apply our algorithm in adaptive data analysis, obtaining an improved guarantee for answering multiple queries regarding some underlying distribution using a finite sample. Numerical computations show that the bounded-noise mechanism outperforms the Gaussian mechanism in many standard settings.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Dec 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 4; 2024: 4; 2025: 1; 2026: 4.

15 papers.

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  1. 2023Subsampling Suffices for Adaptive Data Analysiscited by 9
  2. 2021A Central Limit Theorem for Differentially Private Query Answeringcited by 7
  3. 2023Adaptive Data Analysis in a Balanced Adversarial Modelcited by 5
  4. 2023Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivitycited by 3
  5. 2024Fingerprinting Codes Meet Geometry: Improved Lower Bounds for Private Query Release and Adaptive Data Analysiscited by 3
  6. 2024Privacy Amplification for the Gaussian Mechanism via Bounded Supportcited by 2
  7. 2023AnoFel: Supporting Anonymity for Privacy-Preserving Federated Learningcited by 1
  8. 2025Privately Estimating Black-Box Statisticscited by 1
  9. 2022Private Multi-Winner Voting for Machine Learningcited by 0
  10. 2024Differentially Private Federated Learning without Noise Addition: When is it Possible?cited by 0
  11. 2024Predicting adaptively chosen observables in quantum systemscited by 0
  12. 2026Efficient Adaptive Data Analysis over Dense Distributionscited by 0
3 more
  1. 2026Fixed-Parameter Tractability of Private Synthetic Data Generationcited by 0
  2. 2026Publicly-Verifiable Certificates for Statistical Algorithmscited by 0
  3. 2026How Reusable Are Benchmarks with Richer Feedback?cited by 0