Lightweight Techniques for Private Heavy Hitters
Not on the map. Why?
Abstract (arXiv)
This paper presents Poplar, a new system for solving the private heavy-hitters problem. In this problem, there are many clients and a small set of data-collection servers. Each client holds a private bitstring. The servers want to recover the set of all popular strings, without learning anything else about any client's string. A web-browser vendor, for instance, can use Poplar to figure out which homepages are popular, without learning any user's homepage. We also consider the simpler private subset-histogram problem, in which the servers want to count how many clients hold strings in a particular set without revealing this set to the clients. Poplar uses two data-collection servers and, in a protocol run, each client send sends only a single message to the servers. Poplar protects client privacy against arbitrary misbehavior by one of the servers and our approach requires no public-key cryptography (except for secure channels), nor general-purpose multiparty computation. Instead, we rely on incremental distributed point functions, a new cryptographic tool that allows a client to succinctly secret-share the labels on the nodes of an exponentially large binary tree, provided that the tree has a single non-zero path. Along the way, we develop new general tools for providing malicious security in applications of distributed point functions.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Dec 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 1; 2023: 4; 2024: 2; 2025: 2; 2026: 1.
13 papers.
Sort:
- 2021RoFL: Robustness of Secure Federated Learningcited by 23
- 2023Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learningcited by 16
- 2022SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Servicecited by 10
- 2023Federated Analytics: A surveycited by 9
- 2023Samplable Anonymous Aggregation for Private Federated Data Analysiscited by 6
- 2021Masked LARk: Masked Learning, Aggregation and Reporting worKflowcited by 2
- 2023Differentially Private Heavy Hitter Detection using Federated Analyticscited by 2
- 2024PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicalitycited by 1
- 2025PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectorscited by 1
- 2021Practical and Light-weight Secure Aggregation for Federated Submodel Learningcited by 0
- 2024Fluent: Round-efficient Secure Aggregation for Private Federated Learningcited by 0
- 2025DP-Hype: Federated Differentially Private Hyperparameter Searchcited by 0
1 more
- 2026Robust Estimation of Sparse Numerical Vectors under Local Differential Privacy30 Jul 2026
- 2025DP-Hype: Federated Differentially Private Hyperparameter Search6 Oct 2025
- 2025PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectors14 Mar 2025
- 2024PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicality3 Dec 2024
- 2024Fluent: Round-efficient Secure Aggregation for Private Federated Learning10 Mar 2024
- 2023Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning19 Aug 2023
- 2023Samplable Anonymous Aggregation for Private Federated Data Analysis27 Jul 2023
- 2023Differentially Private Heavy Hitter Detection using Federated Analytics21 Jul 2023
- 2023Federated Analytics: A survey2 Feb 2023
- 2022SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service16 Feb 2022
- 2021Practical and Light-weight Secure Aggregation for Federated Submodel Learning2 Nov 2021
- 2021Masked LARk: Masked Learning, Aggregation and Reporting worKflow27 Oct 2021
1 more
- 2021RoFL: Robustness of Secure Federated Learning7 Jul 2021
- 2021RoFL: Robustness of Secure Federated Learningcited by 23
- 2023Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learningcited by 16
- 2022SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Servicecited by 10
- 2023Federated Analytics: A surveycited by 9
- 2023Samplable Anonymous Aggregation for Private Federated Data Analysiscited by 6
- 2021Masked LARk: Masked Learning, Aggregation and Reporting worKflowcited by 2
- 2023Differentially Private Heavy Hitter Detection using Federated Analyticscited by 2
- 2024PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicalitycited by 1
- 2025PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectorscited by 1
- 2021Practical and Light-weight Secure Aggregation for Federated Submodel Learningcited by 0
- 2024Fluent: Round-efficient Secure Aggregation for Private Federated Learningcited by 0
- 2025DP-Hype: Federated Differentially Private Hyperparameter Searchcited by 0
12 most cited of 13 All 13 (JSON)
- 2026Robust Estimation of Sparse Numerical Vectors under Local Differential Privacy30 Jul 2026
- 2025DP-Hype: Federated Differentially Private Hyperparameter Search6 Oct 2025
- 2025PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectors14 Mar 2025
- 2024PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicality3 Dec 2024
- 2024Fluent: Round-efficient Secure Aggregation for Private Federated Learning10 Mar 2024
- 2023Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning19 Aug 2023
- 2023Samplable Anonymous Aggregation for Private Federated Data Analysis27 Jul 2023
- 2023Differentially Private Heavy Hitter Detection using Federated Analytics21 Jul 2023
- 2023Federated Analytics: A survey2 Feb 2023
- 2022SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service16 Feb 2022
- 2021Practical and Light-weight Secure Aggregation for Federated Submodel Learning2 Nov 2021
- 2021Masked LARk: Masked Learning, Aggregation and Reporting worKflow27 Oct 2021
12 newest of 13 All 13 (JSON)