Research map / Federated learning and privacy / Differential privacy
Differentially private clustering
Give approximation algorithms for private k-means and clustering.
41 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2017–2026) · an approach in Differential privacy, keyword cluster “estimation · error · algorithms”
State History Tried Preprints Who Export
Where it stands
Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.
9 papers in 2023–24 and 12 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic held steady, from 4.2% to 4.5%.
How this is decided
A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.
How it got here
Most cited paper of each year
- 2017 · Differentially Private Clustering in High-Dimensional Euclidean Spaces (ICML 2017 · 43 citations)
- 2018 · Privacy Preserving Point-of-Interest Recommendation Using Decentralized Matrix Factorization (AAAI 2018 · 72 citations)
- 2019 · On Differentially Private Graph Sparsification and Applications (NeurIPS 2019 · 19 citations)
- 2020 · Differentially Private Set Union (ICML 2020 · 16 citations)
- 2021 · Differentially Private Clustering via Maximum Coverage (AAAI 2021 · 16 citations)
- 2022 · Private Rank Aggregation in Central and Local Models (AAAI 2022 · 6 citations)
- 2023 · Sketch-Flip-Merge: Mergeable Sketches for Private Distinct Counting (ICML 2023 · 1 citation)
- 2024 · A Differentially Private Clustering Algorithm for Well-Clustered Graphs (ICLR 2024 · 0 citations)
- 2025 · Breaking the $n^{1.5}$ Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition (ICML 2025 · 0 citations)
- 2026 · Differentially Private Range Subgraph Counting (ICML 2026 · 0 citations)
Earliest papers here
- Differentially Private Clustering in High-Dimensional Euclidean Spaces (ICML 2017 · 43 citations)
- Privacy Preserving Point-of-Interest Recommendation Using Decentralized Matrix Factorization (AAAI 2018 · 72 citations)
- Differentially Private k-Means with Constant Multiplicative Error (NeurIPS 2018 · 28 citations)
What has been tried
30 papers here report 169 measured changes (a component added, removed or swapped, compared with a baseline): helped 71%, no clear effect 5%, hurt 18%, mixed 6%. Papers mostly publish changes that helped, so "helped" is over-represented.
Ablations in recent papers
| What was changed | Measured on | Result | Effect | Paper |
|---|---|---|---|---|
| Vary the maximum adaptive degree d_max | Reddit · output size | No clear effect | Scalable Private Partition Selection via Adaptive Weighting (ICML 2025 · 0 citations) |
Recent preprints
Loading recent arXiv preprints…
Who publishes on it
Institutions on the most papers of this approach since 2023 (of 21 papers; from OpenAlex and the papers' affiliations).
- University of Copenhagen · 3 papers
- Morgan Stanley · 3 papers
- Google · 3 papers
- Institute of Science and Technology · 2 papers
- University of Virginia · 2 papers
- University of California, San Diego · 2 papers
- University of Wisconsin - Madison · 2 papers
- Massachusetts Institute of Technology · 2 papers
Use it
Download BibTeX of 35 key papersOpen the topic in the interactive map
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Related approaches
Same keyword cluster
- Privacy accounting and mechanisms (127 papers)
- Private online and user-level learning (116 papers)
- Local-DP and private estimation (113 papers)
- Private data analysis frameworks (105 papers)
Elsewhere in the topic
- Private training with public data (96 papers)
- Differentially private synthetic data (81 papers)
- DP-SGD refinements (60 papers)
- Differentially private federated learning (35 papers)
Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).