BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model
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Abstract (arXiv)
We propose a hybrid model of differential privacy that considers a combination of regular and opt-in users who desire the differential privacy guarantees of the local privacy model and the trusted curator model, respectively. We demonstrate that within this model, it is possible to design a new type of blended algorithm for the task of privately computing the head of a search log. This blended approach provides significant improvements in the utility of obtained data compared to related work while providing users with their desired privacy guarantees. Specifically, on two large search click data sets, comprising 1.75 and 16 GB respectively, our approach attains NDCG values exceeding 95% across a range of privacy budget values.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 May 2017.
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Citing papers per year: 2021: 4; 2022: 3; 2023: 4; 2024: 3.
14 papers.
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- 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
- 2021Public Data-Assisted Mirror Descent for Private Model Trainingcited by 49
- 2022Private Estimation with Public Datacited by 31
- 2023Private Distribution Learning with Public Data: The View from Sample Compressioncited by 14
- 2024Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networkscited by 9
- 2024Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attackscited by 8
- 2023Mean Estimation Under Heterogeneous Privacy: Some Privacy Can Be Freecited by 6
- 2023Mean Estimation Under Heterogeneous Privacy Demandscited by 6
- 2021Federated Learning with Heterogeneous Differential Privacycited by 5
- 2022Transfer Learning In Differential Privacy's Hybrid-Modelcited by 3
- 2021Locally Differentially Private Bayesian Inferencecited by 1
- 2023Federated Learning with Reduced Information Leakage and Computationcited by 1
- 2024Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks22 Oct 2024
- 2024Amalgam: A Framework for Obfuscated Neural Network Training on the Cloud2 Jun 2024
- 2024Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks22 Apr 2024
- 2023Mean Estimation Under Heterogeneous Privacy Demands19 Oct 2023
- 2023Federated Learning with Reduced Information Leakage and Computation10 Oct 2023
- 2023Private Distribution Learning with Public Data: The View from Sample Compression11 Aug 2023
- 2023Mean Estimation Under Heterogeneous Privacy: Some Privacy Can Be Free27 Apr 2023
- 2022Momentum Gradient Descent Federated Learning with Local Differential Privacy28 Sep 2022
- 2022Private Estimation with Public Data16 Aug 2022
- 2022Transfer Learning In Differential Privacy's Hybrid-Model28 Jan 2022
- 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling21 Dec 2021
- 2021Public Data-Assisted Mirror Descent for Private Model Training1 Dec 2021
2 more
- 2021Federated Learning with Heterogeneous Differential Privacy28 Oct 2021
- 2021Locally Differentially Private Bayesian Inference27 Oct 2021
- 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
- 2021Public Data-Assisted Mirror Descent for Private Model Trainingcited by 49
- 2022Private Estimation with Public Datacited by 31
- 2023Private Distribution Learning with Public Data: The View from Sample Compressioncited by 14
- 2024Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networkscited by 9
- 2024Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attackscited by 8
- 2023Mean Estimation Under Heterogeneous Privacy: Some Privacy Can Be Freecited by 6
- 2023Mean Estimation Under Heterogeneous Privacy Demandscited by 6
- 2021Federated Learning with Heterogeneous Differential Privacycited by 5
- 2022Transfer Learning In Differential Privacy's Hybrid-Modelcited by 3
- 2021Locally Differentially Private Bayesian Inferencecited by 1
- 2023Federated Learning with Reduced Information Leakage and Computationcited by 1
12 most cited of 14 All 14 (JSON)
- 2024Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks22 Oct 2024
- 2024Amalgam: A Framework for Obfuscated Neural Network Training on the Cloud2 Jun 2024
- 2024Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks22 Apr 2024
- 2023Mean Estimation Under Heterogeneous Privacy Demands19 Oct 2023
- 2023Federated Learning with Reduced Information Leakage and Computation10 Oct 2023
- 2023Private Distribution Learning with Public Data: The View from Sample Compression11 Aug 2023
- 2023Mean Estimation Under Heterogeneous Privacy: Some Privacy Can Be Free27 Apr 2023
- 2022Momentum Gradient Descent Federated Learning with Local Differential Privacy28 Sep 2022
- 2022Private Estimation with Public Data16 Aug 2022
- 2022Transfer Learning In Differential Privacy's Hybrid-Model28 Jan 2022
- 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling21 Dec 2021
- 2021Public Data-Assisted Mirror Descent for Private Model Training1 Dec 2021
12 newest of 14 All 14 (JSON)