A Joint Exponential Mechanism For Differentially Private Top-k
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Abstract (arXiv)
We present a differentially private algorithm for releasing the sequence of k elements with the highest counts from a data domain of d elements. The algorithm is a "joint" instance of the exponential mechanism, and its output space consists of all O(dk) length-k sequences. Our main contribution is a method to sample this exponential mechanism in time O(dklog(k) + dlog(d)) and space O(dk). Experiments show that this approach outperforms existing pure differential privacy methods and improves upon even approximate differential privacy methods for moderate k.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jan 2022.
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Citing papers per year: 2022: 2; 2023: 2; 2024: 3; 2025: 2.
9 papers.
- 2023Privacy-Preserving In-Context Learning for Large Language Modelscited by 50
- 2024Privacy Preserving Prompt Engineering: A Surveycited by 24
- 2024Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternativescited by 12
- 2022Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATEcited by 9
- 2023Better Private Linear Regression Through Better Private Feature Selectioncited by 5
- 2022Differentially Private Top-k Selection via Canonical Lipschitz Mechanismcited by 2
- 2024Differentially Private Prototypes for Imbalanced Transfer Learningcited by 2
- 2025DP-GTR: Differentially Private Prompt Protection via Group Text Rewritingcited by 2
- 2025Differentially Private In-Context Learning with Nearest Neighbor Searchcited by 1
- 2025Differentially Private In-Context Learning with Nearest Neighbor Search6 Nov 2025
- 2025DP-GTR: Differentially Private Prompt Protection via Group Text Rewriting6 Mar 2025
- 2024Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives2 Nov 2024
- 2024Differentially Private Prototypes for Imbalanced Transfer Learning12 Jun 2024
- 2024Privacy Preserving Prompt Engineering: A Survey9 Apr 2024
- 2023Better Private Linear Regression Through Better Private Feature Selection1 Jun 2023
- 2023Privacy-Preserving In-Context Learning for Large Language Models2 May 2023
- 2022Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE30 Mar 2022
- 2022Differentially Private Top-k Selection via Canonical Lipschitz Mechanism31 Jan 2022