A Joint Exponential Mechanism For Differentially Private Top-k
Jennifer Gillenwater, Matthew Joseph, Andres Muñoz Medina, Mónica Ribero Diaz
Abstract
We present a differentially private algorithm for releasing the sequence of elements with the highest counts from a data domain of elements. The algorithm is a"joint"instance of the exponential mechanism, and its output space consists of all length- sequences. Our main contribution is a method to sample this exponential mechanism in time and space . Experiments show that this approach outperforms existing pure differential privacy methods and improves upon even approximate differential privacy methods for moderate .
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext ac5ae7cd-39e1-44b3-9e3c-e20ec867f60bCited by top-tier papers9
- Privacy-Preserving In-Context Learning for Large Language ModelsTong Wu, Ashwinee Panda, Jiachen T. Wang, Prateek MittalICLR 2024 · 58 citations
- Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed AlternativesVincent Hanke, Tom Blanchard, Franziska Boenisch, Iyiola E. Olatunji et al.NeurIPS 2024 · 27 citations
- Local Differentially Private Heavy Hitter Detection in Data Streams with Bounded MemoryXiaochen Li, Weiran Liu, Jian Lou, Yuan Hong et al.SIGMOD 2024 · 13 citations
- Better Private Linear Regression Through Better Private Feature SelectionTravis Dick, Jennifer Gillenwater, Matthew JosephNeurIPS 2023 · 7 citations
- Differentially Private Prototypes for Imbalanced Transfer LearningDariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska BoenischAAAI 2025 · 4 citations
Builds on5
- Permute-and-Flip: A new mechanism for differentially private selectionRyan McKenna, Daniel SheldonNeurIPS 2020 · 66 citations
- Differentially Private Password Frequency ListsJeremiah Blocki, Anupam Datta, Joseph BonneauNDSS 2016 · 62 citations
- Oneshot Differentially Private Top-k SelectionGang Qiao, Weijie J. Su, Li ZhangICML 2021 · 40 citations
- Free Gap Information from the Differentially Private Sparse Vector and Noisy Max MechanismsZeyu Ding, Yuxin Wang, Danfeng Zhang, Dan KiferVLDB 2020 · 14 citations
- Differentially Private QuantilesJennifer Gillenwater, Matthew Joseph, Alex KuleszaICML 2021 · 2 citations
Related papers
- Faster Differentially Private Top-k Selection: A Joint Exponential Mechanism with PruningHao Wu, Hanwen ZhangNeurIPS 2024 · 3 citations
- Privately Counting Partially Ordered DataMatthew Joseph, Mónica Ribero, Alexander YuICLR 2025
- Tight Data Access Bounds for Private Top-k SelectionHao Wu, Olga Ohrimenko, Anthony WirthICML 2023
- Differentially Private Quantiles with Smaller ErrorJacob Imola, Fabrizio Boninsegna, Hannah Keller, Anders Aamand et al.NeurIPS 2025 · 4 citations
- Efficient mean estimation with pure differential privacy via a sum-of-squares exponential mechanismSamuel B. Hopkins, Gautam Kamath, Mahbod MajidSTOC 2022 · 20 citations
