Bipartite Randomized Response Mechanism for Local Differential Privacy

• URL: http://arxiv.org/abs/2504.20926v2
• Date: Mon, 29 Sep 2025 08:31:41 GMT
• Title: Bipartite Randomized Response Mechanism for Local Differential Privacy
• Authors: Shun Zhang, Hai Zhu, Zhili Chen, Haibo Hu,
• Abstract summary: Local Differential Privacy (LDP) has recently become a strong measure of privacy for protecting each user's privacy from data analysts.<n>Our goal is to design a differentially private mechanism that releases an item with the global expected error as small as possible.
• Score: 14.32248634830699
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the increasing importance of data privacy, Local Differential Privacy (LDP) has recently become a strong measure of privacy for protecting each user's privacy from data analysts without relying on a trusted third party. In this paper, we consider the problem of high-utility differentially private release. Given a domain of finite integers {1,2,...,N} and a distance-defined utility function, our goal is to design a differentially private mechanism that releases an item with the global expected error as small as possible. The most common LDP mechanism for this task is the Generalized Randomized Response (GRR) mechanism that treats all candidates equally except for the true item. In this paper, we introduce Bipartite Randomized Response mechanism (BRR), which adaptively divides all candidates into two parts by utility rankings given priori item. In the local search phase, we confirm how many high-utility candidates to be assigned with high release probability as the true item, which gives the locally optimal bipartite classification of all candidates. For preserving LDP, the global search phase uniformly selects the smallest number of dynamic high-utility candidates obtained locally. In particular, we give explicit formulas on the uniform number of dynamic high-utility candidates. The global expected error of our BRR is always no larger than the GRR, and can offer a decrease with a small and asymptotically exact factor. Extensive experiments demonstrate that BRR outperforms the state-of-the-art methods across the standard metrics and datasets.

Related papers

• Differentially Private High-dimensional Variable Selection via Integer Programming [21.497279758328872]
  Sparse variable selection improves interpretability and in high-dimensional learning by selecting a small subset of informative features.<n>Recent advances in Mixed Programming have enabled solving large-scale non-private regression.<n>We introduce two new pure differentially private variable selection, levering MIP.
  arXiv  Detail & Related papers  (2025-10-24T22:57:33Z)
• Judging with Confidence: Calibrating Autoraters to Preference Distributions [56.17041629492863]
  We argue that a reliable autorater must learn to model the full distribution of preferences defined by a target population.<n>We present two learning methods tailored to different data conditions.<n>Our results show that finetuning autoraters with a distribution-matching objective leads to verbalized probability predictions that are better aligned with the target preference distribution.
  arXiv  Detail & Related papers  (2025-09-30T20:36:41Z)
• Frequency Estimation of Correlated Multi-attribute Data under Local Differential Privacy [5.258253745672122]
  Local Differential Privacy (LDP) is a powerful tool to protect user data privacy.<n>Existing LDP mechanisms either split the privacy budget across all attributes or treat each attribute independently.<n>We propose Correlated Randomized Response (Corr-RR), a novel LDP mechanism that leverages correlations among attributes to substantially improve utility.
  arXiv  Detail & Related papers  (2025-07-23T13:52:45Z)
• Optimal Allocation of Privacy Budget on Hierarchical Data Release [48.96399034594329]
  This paper addresses the problem of optimal privacy budget allocation for hierarchical data release.<n>It aims to maximize data utility subject to a total privacy budget while considering the inherent trade-offs between data granularity and privacy loss.
  arXiv  Detail & Related papers  (2025-05-16T05:25:11Z)
• Locally Differentially Private Frequency Estimation via Joint Randomized Response [14.398663407098836]
  Local Differential Privacy (LDP) has been widely recognized as a powerful tool for providing a strong theoretical guarantee of data privacy to data contributors against an untrusted data collector.<n>Common to existing LDP mechanisms is an inherent trade-off between the level of privacy protection and data utility.<n>We propose a novel Joint Randomized Response (JRR) mechanism based on correlated data perturbations to achieve locally differentially private frequency estimation.
  arXiv  Detail & Related papers  (2025-05-15T14:41:03Z)
• From Randomized Response to Randomized Index: Answering Subset Counting Queries with Local Differential Privacy [27.59934932590226]
  Local Differential Privacy (LDP) is the predominant privacy model for safeguarding individual data privacy.<n>We propose an alternative approach -- instead of perturbing values, we apply randomization to indexes of values.<n>Inspired by the deniability of randomized indexes, we present CRIAD for answering subset counting queries on set-value data.
  arXiv  Detail & Related papers  (2025-04-24T13:08:11Z)
• Private Selection with Heterogeneous Sensitivities [2.0936724675062406]
  Differentially private (DP) selection involves choosing a high-scoring candidate from a finite candidate pool.<n>This problem arises naturally in a variety of contexts including model selection, hypothesis testing, and within many DP algorithms.<n>To address this, algorithms like the Generalised Exponential Mechanism (GEM) leverage variability in candidate sensitivities.
  arXiv  Detail & Related papers  (2025-01-09T15:25:07Z)
• Differentially Private Random Feature Model [52.468511541184895]
  We produce a differentially private random feature model for privacy-preserving kernel machines.<n>We show that our method preserves privacy and derive a generalization error bound for the method.
  arXiv  Detail & Related papers  (2024-12-06T05:31:08Z)
• Pseudo-Probability Unlearning: Towards Efficient and Privacy-Preserving Machine Unlearning [59.29849532966454]
  We propose PseudoProbability Unlearning (PPU), a novel method that enables models to forget data to adhere to privacy-preserving manner. Our method achieves over 20% improvements in forgetting error compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2024-11-04T21:27:06Z)
• Enhanced Privacy Bound for Shuffle Model with Personalized Privacy [32.08637708405314]
  Differential Privacy (DP) is an enhanced privacy protocol which introduces an intermediate trusted server between local users and a central data curator. It significantly amplifies the central DP guarantee by anonymizing and shuffling the local randomized data. This work focuses on deriving the central privacy bound for a more practical setting where personalized local privacy is required by each user.
  arXiv  Detail & Related papers  (2024-07-25T16:11:56Z)
• AAA: an Adaptive Mechanism for Locally Differential Private Mean Estimation [42.95927712062214]
  Local differential privacy (LDP) is a strong privacy standard that has been adopted by popular software systems. We propose the advanced adaptive additive (AAA) mechanism, which is a distribution-aware approach that addresses the average utility. We provide rigorous privacy proofs, utility analyses, and extensive experiments comparing AAA with state-of-the-art mechanisms.
  arXiv  Detail & Related papers  (2024-04-02T04:22:07Z)
• Privacy Amplification for the Gaussian Mechanism via Bounded Support [64.86780616066575]
  Data-dependent privacy accounting frameworks such as per-instance differential privacy (pDP) and Fisher information loss (FIL) confer fine-grained privacy guarantees for individuals in a fixed training dataset. We propose simple modifications of the Gaussian mechanism with bounded support, showing that they amplify privacy guarantees under data-dependent accounting.
  arXiv  Detail & Related papers  (2024-03-07T21:22:07Z)
• Optimal Unbiased Randomizers for Regression with Label Differential Privacy [61.63619647307816]
  We propose a new family of label randomizers for training regression models under the constraint of label differential privacy (DP) We demonstrate that these randomizers achieve state-of-the-art privacy-utility trade-offs on several datasets.
  arXiv  Detail & Related papers  (2023-12-09T19:58:34Z)
• Bounded and Unbiased Composite Differential Privacy [25.427802467876248]
  The objective of differential privacy (DP) is to protect privacy by producing an output distribution that is indistinguishable between two neighboring databases. Existing solutions attempt to address this issue by employing post-processing or truncation techniques. We propose a novel differentially private mechanism which uses a composite probability density function to generate bounded and unbiased outputs.
  arXiv  Detail & Related papers  (2023-11-04T04:43:47Z)
• On the Privacy-Robustness-Utility Trilemma in Distributed Learning [7.778461949427662]
  We present the first tight analysis of the error incurred by any algorithm ensuring robustness against a fraction of adversarial machines. Our analysis exhibits a fundamental trade-off between privacy, robustness, and utility.
  arXiv  Detail & Related papers  (2023-02-09T17:24:18Z)
• FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations [53.268801169075836]
  We propose FedLAP-DP, a novel privacy-preserving approach for federated learning. A formal privacy analysis demonstrates that FedLAP-DP incurs the same privacy costs as typical gradient-sharing schemes. Our approach presents a faster convergence speed compared to typical gradient-sharing methods.
  arXiv  Detail & Related papers  (2023-02-02T12:56:46Z)
• DP2-Pub: Differentially Private High-Dimensional Data Publication with Invariant Post Randomization [58.155151571362914]
  We propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases. splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable privacy budget. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy.
  arXiv  Detail & Related papers  (2022-08-24T17:52:43Z)
• Production of Categorical Data Verifying Differential Privacy: Conception and Applications to Machine Learning [0.0]
  Differential privacy is a formal definition that allows quantifying the privacy-utility trade-off. With the local DP (LDP) model, users can sanitize their data locally before transmitting it to the server. In all cases, we concluded that differentially private ML models achieve nearly the same utility metrics as non-private ones.
  arXiv  Detail & Related papers  (2022-04-02T12:50:14Z)
• Smoothed Differential Privacy [55.415581832037084]
  Differential privacy (DP) is a widely-accepted and widely-applied notion of privacy based on worst-case analysis. In this paper, we propose a natural extension of DP following the worst average-case idea behind the celebrated smoothed analysis. We prove that any discrete mechanism with sampling procedures is more private than what DP predicts, while many continuous mechanisms with sampling procedures are still non-private under smoothed DP.
  arXiv  Detail & Related papers  (2021-07-04T06:55:45Z)
• Lossless Compression of Efficient Private Local Randomizers [55.657133416044104]
  Locally Differentially Private (LDP) Reports are commonly used for collection of statistics and machine learning in the federated setting. In many cases the best known LDP algorithms require sending prohibitively large messages from the client device to the server. This has led to significant efforts on reducing the communication cost of LDP algorithms.
  arXiv  Detail & Related papers  (2021-02-24T07:04:30Z)
• Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning [34.79337646727395]
  This paper introduces the first provably accurate algorithms for differentially private, top-down decision tree learning in the distributed setting. We propose DP-TopDown, a general privacy preserving decision tree learning algorithm, and present two distributed implementations.
  arXiv  Detail & Related papers  (2020-12-19T06:09:36Z)
• Graph-Homomorphic Perturbations for Private Decentralized Learning [64.26238893241322]
  Local exchange of estimates allows inference of data based on private data. perturbations chosen independently at every agent, resulting in a significant performance loss. We propose an alternative scheme, which constructs perturbations according to a particular nullspace condition, allowing them to be invisible.
  arXiv  Detail & Related papers  (2020-10-23T10:35:35Z)
• A One-Pass Private Sketch for Most Machine Learning Tasks [48.17461258268463]
  Differential privacy (DP) is a compelling privacy definition that explains the privacy-utility tradeoff via formal, provable guarantees. We propose a private sketch that supports a multitude of machine learning tasks including regression, classification, density estimation, and more. Our sketch consists of randomized contingency tables that are indexed with locality-sensitive hashing and constructed with an efficient one-pass algorithm.
  arXiv  Detail & Related papers  (2020-06-16T17:47:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.