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Local privacy and statistical minimax rates

https://doi.org/10.1109/allerton.2013.6736718

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/11681878_14openalex-snapshot

Calibrating Noise to Sensitivity in Private Data Analysis

inferredcites →https://doi.org/10.1080/01621459.1965.10480775openalex-snapshot

Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias

inferredcites →https://doi.org/10.1109/focs.2013.53openalex-snapshot

Local Privacy and Statistical Minimax Rates

inferredcites →https://doi.org/10.1145/773153.773174openalex-snapshot

Limiting privacy breaches in privacy preserving data mining

inferredcites →https://doi.org/10.1214/aos/1017939142openalex-snapshot

Information-theoretic determination of minimax rates of convergence

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Local private ordinal data distribution estimation

inferred← citeshttps://doi.org/10.1109/infocom.2018.8486234openalex-snapshot

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Calibrate: Frequency Estimation and Heavy Hitter Identification with Local Differential Privacy via Incorporating Prior Knowledge

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AdaPDP: Adaptive Personalized Differential Privacy

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Stateful Switch: Optimized Time Series Release with Local Differential Privacy

inferred← citeshttps://doi.org/10.1109/iscc58397.2023.10218003openalex-snapshot

Differentially Private Functional Mechanism for Broad Learning System

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Optimal schemes for discrete distribution estimation under local differential privacy

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Smart meter privacy based on adversarial hypothesis testing

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Distributed Statistical Estimation of High-Dimensional and Nonparametric Distributions

inferred← citeshttps://doi.org/10.1109/isit44484.2020.9173952openalex-snapshot

Linear and Range Counting under Metric-based Local Differential Privacy

inferred← citeshttps://doi.org/10.1109/isit44484.2020.9173959openalex-snapshot

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Classical Mechanism is Optimal in Classical-Quantum Differentially Private Mechanisms

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An Information-Theoretic Characterization of Pufferfish Privacy

inferred← citeshttps://doi.org/10.1109/isit62367.2026.11653807openalex-snapshot

Dobrushin Coefficients of Private Mechanisms Beyond Local Differential Privacy

inferred← citeshttps://doi.org/10.1109/isssr61934.2024.00017openalex-snapshot

Secure Searchable Encryption Based on Local Differential Privacy

inferred← citeshttps://doi.org/10.1109/ithings-greencom-cpscom-smartdata-cybermatics50389.2020.00125openalex-snapshot

Differentially Private Machine Learning Model against Model Extraction Attack

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Straggler-Resilient Differentially-Private Decentralized Learning

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Differential Privacy-Preserving User Linkage across Online Social Networks

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Blockchain-Enabled Contextual Online Learning Under Local Differential Privacy for Coronary Heart Disease Diagnosis in Mobile Edge Computing

inferred← citeshttps://doi.org/10.1109/jiot.2019.2902815openalex-snapshot

Deep ${Q}$ -Network-Based Route Scheduling for TNC Vehicles With Passengers’ Location Differential Privacy

inferred← citeshttps://doi.org/10.1109/jiot.2021.3101991openalex-snapshot

Differential Privacy Meets Federated Learning Under Communication Constraints

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Inference Under Information Constraints III: Local Privacy Constraints

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A Compression Perspective on Secrecy Measures

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Shuffled Model of Federated Learning: Privacy, Accuracy and Communication Trade-Offs

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The Capacity of Single-Server Weakly-Private Information Retrieval

inferred← citeshttps://doi.org/10.1109/jsait.2023.3287929openalex-snapshot

Active Privacy-Utility Trade-Off Against Inference in Time-Series Data Sharing

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Toward Owners’ Control in Digital Data Markets

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A Survey on Statistical, Information, and Estimation—Theoretic Views on Privacy

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Mobile Data Collection and Analysis with Local Differential Privacy

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Distributed Cardinality Estimation of Set Operations with Differential Privacy

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The Utility Optimization of Type I-PTT for Satellite Internet Navigation Location Information Protection

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Locally Differentially Private Frequent Itemset Mining

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PrivKV: Key-Value Data Collection with Local Differential Privacy

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inferred← citeshttps://doi.org/10.1109/tac.2016.2541298openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/tc.2016.2595562openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/tdsc.2019.2949041openalex-snapshot

Secure and Utility-Aware Data Collection with Condensed Local Differential Privacy

inferred← citeshttps://doi.org/10.1109/tdsc.2022.3186023openalex-snapshot

Bilateral Privacy-Preserving Worker Selection in Spatial Crowdsourcing

inferred← citeshttps://doi.org/10.1109/tdsc.2022.3227654openalex-snapshot

Differentially Private Distributed Frequency Estimation

inferred← citeshttps://doi.org/10.1109/tetc.2019.2959581openalex-snapshot

Joint Distribution Estimation and Naïve Bayes Classification Under Local Differential Privacy

inferred← citeshttps://doi.org/10.1109/tifs.2018.2812146openalex-snapshot

$\textsf{LoPub}$ : High-Dimensional Crowdsourced Data Publication With Local Differential Privacy

inferred← citeshttps://doi.org/10.1109/tifs.2019.2903660openalex-snapshot

Development and Analysis of Deterministic Privacy-Preserving Policies Using Non- Stochastic Information Theory

inferred← citeshttps://doi.org/10.1109/tifs.2019.2916650openalex-snapshot

Decentralized Detection With Robust Information Privacy Protection

inferred← citeshttps://doi.org/10.1109/tifs.2019.2930366openalex-snapshot

Compressive Privacy for a Linear Dynamical System

inferred← citeshttps://doi.org/10.1109/tifs.2020.2985524openalex-snapshot

AsgLDP: Collecting and Generating Decentralized Attributed Graphs With Local Differential Privacy

inferred← citeshttps://doi.org/10.1109/tifs.2020.3002479openalex-snapshot

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inferred← citeshttps://doi.org/10.1109/tifs.2023.3289007openalex-snapshot

Collaborative Sampling for Partial Multi-Dimensional Value Collection Under Local Differential Privacy

inferred← citeshttps://doi.org/10.1109/tifs.2023.3303718openalex-snapshot

TEAR: Exploring Temporal Evolution of Adversarial Robustness for Membership Inference Attacks Against Federated Learning

inferred← citeshttps://doi.org/10.1109/tifs.2024.3354412openalex-snapshot

Approaching the Information-Theoretic Limit of Privacy Disclosure With Utility Guarantees

inferred← citeshttps://doi.org/10.1109/tifs.2024.3449556openalex-snapshot

Extremal Mechanisms for Pointwise Maximal Leakage

inferred← citeshttps://doi.org/10.1109/tifs.2025.3571661openalex-snapshot

Side-Channel Attacks and New Principles in the Shuffle Model of Differential Privacy

inferred← citeshttps://doi.org/10.1109/tifs.2026.3655155openalex-snapshot

LFS: A Locally Private Framework for Degree Statistic Estimation With Laplace Mechanism

inferred← citeshttps://doi.org/10.1109/tit.2018.2809790openalex-snapshot

Optimal Schemes for Discrete Distribution Estimation Under Locally Differential Privacy

inferred← citeshttps://doi.org/10.1109/tit.2019.2935768openalex-snapshot

Tunable Measures for Information Leakage and Applications to Privacy-Utility Tradeoffs

inferred← citeshttps://doi.org/10.1109/tit.2020.3041224openalex-snapshot

Secrecy by Design With Applications to Privacy and Compression

inferred← citeshttps://doi.org/10.1109/tit.2021.3120371openalex-snapshot

A Graph Symmetrization Bound on Channel Information Leakage Under Blowfish Privacy

inferred← citeshttps://doi.org/10.1109/tit.2021.3126865openalex-snapshot

Multi-Server Weakly-Private Information Retrieval

inferred← citeshttps://doi.org/10.1109/tit.2023.3296288openalex-snapshot

Pufferfish Privacy: An Information-Theoretic Study

inferred← citeshttps://doi.org/10.1109/tit.2023.3304378openalex-snapshot

Pointwise Maximal Leakage

inferred← citeshttps://doi.org/10.1109/tit.2023.3309920openalex-snapshot

A Few Interactions Improve Distributed Nonparametric Estimation, Optimally

inferred← citeshttps://doi.org/10.1109/tit.2023.3341148openalex-snapshot

An Operational Approach to Information Leakage via Generalized Gain Functions

inferred← citeshttps://doi.org/10.1109/tit.2024.3383398openalex-snapshot

Vector Quantization With Error Uniformly Distributed Over an Arbitrary Set

inferred← citeshttps://doi.org/10.1109/tkde.2017.2697856openalex-snapshot

Differentially Private Data Publishing and Analysis: A Survey

inferred← citeshttps://doi.org/10.1109/tkde.2018.2805356openalex-snapshot

Privacy Enhanced Matrix Factorization for Recommendation with Local Differential Privacy

inferred← citeshttps://doi.org/10.1109/tkde.2018.2846640openalex-snapshot

Privacy Engineering for the Smart Micro-Grid

inferred← citeshttps://doi.org/10.1109/tkde.2018.2885749openalex-snapshot

Learning New Words from Keystroke Data with Local Differential Privacy

inferred← citeshttps://doi.org/10.1109/tkde.2020.2963977openalex-snapshot

Privacy-Preserving Stochastic Gradual Learning

inferred← citeshttps://doi.org/10.1109/tkde.2021.3073062openalex-snapshot

Applications of Differential Privacy in Social Network Analysis: A Survey

inferred← citeshttps://doi.org/10.1109/tkde.2022.3186907openalex-snapshot

Collecting Preference Rankings under Local Differential Privacy

inferred← citeshttps://doi.org/10.1109/tkde.2022.3207486openalex-snapshot

Analyzing Preference Data With Local Privacy: Optimal Utility and Enhanced Robustness

inferred← citeshttps://doi.org/10.1109/tkde.2023.3265605openalex-snapshot

DP2-Pub: Differentially Private High-Dimensional Data Publication With Invariant Post Randomization

inferred← citeshttps://doi.org/10.1109/tmc.2020.2972001openalex-snapshot

Protecting Your Shopping Preference With Differential Privacy

inferred← citeshttps://doi.org/10.1109/tmc.2022.3173642openalex-snapshot

Disguised as Privacy: Data Poisoning Attacks against Differentially Private Crowdsensing Systems

inferred← citeshttps://doi.org/10.1109/tmc.2025.3568511openalex-snapshot

Personalized Local Differential Privacy for Multi-Dimensional Range Queries Over Mobile User Data

inferred← citeshttps://doi.org/10.1109/tpds.2019.2899097openalex-snapshot

Local Differential Private Data Aggregation for Discrete Distribution Estimation

inferred← citeshttps://doi.org/10.1109/tpwrs.2016.2628055openalex-snapshot

Architecture and Algorithms for Privacy Preserving Thermal Inertial Load Management by a Load Serving Entity

inferred← citeshttps://doi.org/10.1109/tsp.2018.2793871openalex-snapshot

Toward Information Privacy for the Internet of Things: A Nonparametric Learning Approach

inferred← citeshttps://doi.org/10.1109/tsp.2020.3033483openalex-snapshot

Optimal Local Differentially Private Quantization

inferred← citeshttps://doi.org/10.1109/tsp.2021.3126929openalex-snapshot

Privacy-Preserving Distributed Projection LMS for Linear Multitask Networks

inferred← citeshttps://doi.org/10.1109/twc.2021.3069763openalex-snapshot

Data-Driven Caching With Users’ Content Preference Privacy in Information-Centric Networks

inferred← citeshttps://doi.org/10.1137/1.9781611975994.31openalex-snapshot

Exponential Separations in Local Differential Privacy

inferred← citeshttps://doi.org/10.1145/2976749.2978308openalex-snapshot

Differential Privacy as a Mutual Information Constraint

inferred← citeshttps://doi.org/10.1145/3243734.3243750openalex-snapshot

MVG Mechanism

inferred← citeshttps://doi.org/10.1145/3377555.3377887openalex-snapshot

A study of event frequency profiling with differential privacy

inferred← citeshttps://doi.org/10.1145/3460231.3474262openalex-snapshot

Stronger Privacy for Federated Collaborative Filtering With Implicit Feedback

inferred← citeshttps://doi.org/10.1145/3511808.3557278openalex-snapshot

Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees

inferred← citeshttps://doi.org/10.1145/3658644.3690350openalex-snapshot

AirGapAgent: Protecting Privacy-Conscious Conversational Agents

inferred← citeshttps://doi.org/10.1214/19-ejs1625openalex-snapshot

Optimal locally private estimation under $\ell_{p}$ loss for $1\le p\le 2$

inferred← citeshttps://doi.org/10.1360/ssi-2019-0119openalex-snapshot

Bayesian network-based high-dimensional crowdsourced data publication with local differential privacy

inferred← citeshttps://doi.org/10.14722/autosec.2022.23024openalex-snapshot

Drivers and Passengers Maybe the Weakest Link in the CAV Data Privacy Defenses

inferred← citeshttps://doi.org/10.14722/ndss.2018.23120openalex-snapshot

Revisiting Private Stream Aggregation: Lattice-Based PSA

inferred← citeshttps://doi.org/10.14722/ndss.2023.24444openalex-snapshot

REaaS: Enabling Adversarially Robust Downstream Classifiers via Robust Encoder as a Service

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Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries

inferred← citeshttps://doi.org/10.1609/aaai.v30i1.10254openalex-snapshot

On the Differential Privacy of Bayesian Inference

inferred← citeshttps://doi.org/10.1609/aaai.v32i1.11301openalex-snapshot

Comparing Population Means Under Local Differential Privacy: With Significance and Power

inferred← citeshttps://doi.org/10.1609/aaai.v35i10.17053openalex-snapshot

FLAME: Differentially Private Federated Learning in the Shuffle Model

inferred← citeshttps://doi.org/10.1609/aaai.v35i12.17330openalex-snapshot

Local Differential Privacy for Bayesian Optimization

inferred← citeshttps://doi.org/10.1609/aaai.v35i8.16882openalex-snapshot

Differentially Private Stochastic Coordinate Descent

inferred← citeshttps://doi.org/10.1609/aaai.v36i6.20565openalex-snapshot

Locally Private k-Means Clustering with Constant Multiplicative Approximation and Near-Optimal Additive Error

inferred← citeshttps://doi.org/10.1609/aaai.v36i6.20588openalex-snapshot

Differentially Private Regret Minimization in Episodic Markov Decision Processes

inferred← citeshttps://doi.org/10.1609/aaai.v36i9.21241openalex-snapshot

Local Differential Privacy for Belief Functions

inferred← citeshttps://doi.org/10.1609/aaai.v37i12.26770openalex-snapshot

DPAUC: Differentially Private AUC Computation in Federated Learning

inferred← citeshttps://doi.org/10.1609/aaai.v38i2.27851openalex-snapshot

Disguise without Disruption: Utility-Preserving Face De-identification

inferred← citeshttps://doi.org/10.17760/d20409473openalex-snapshot

Differential privacy in the shuffle model

inferred← citeshttps://doi.org/10.17760/d20413921openalex-snapshot

Differentially private statistical estimation

inferred← citeshttps://doi.org/10.18653/v1/2021.findings-acl.337openalex-snapshot

Differential Privacy for Text Analytics via Natural Text Sanitization

inferred← citeshttps://doi.org/10.18653/v1/2022.emnlp-main.323openalex-snapshot

Differentially Private Language Models for Secure Data Sharing

inferred← citeshttps://doi.org/10.18653/v1/2022.findings-naacl.65openalex-snapshot

The Limits of Word Level Differential Privacy

inferred← citeshttps://doi.org/10.20944/preprints202306.0289.v2openalex-snapshot

Item-Oriented Personalized LDP for Discrete Distribution Estimation

inferred← citeshttps://doi.org/10.21203/rs.3.rs-1890894/v1openalex-snapshot

Efficiency-optimized Data Perturbation in Local Differentially Private Federated Learning

inferred← citeshttps://doi.org/10.23919/isita.2018.8664351openalex-snapshot

On the Anonymization of Differentially Private Location Obfuscation

inferred← citeshttps://doi.org/10.24963/ijcai.2019/665openalex-snapshot

Lower Bound of Locally Differentially Private Sparse Covariance Matrix Estimation

inferred← citeshttps://doi.org/10.24963/ijcai.2019/666openalex-snapshot

Principal Component Analysis in the Local Differential Privacy Model

inferred← citeshttps://doi.org/10.24963/ijcai.2021/217openalex-snapshot

LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy

inferred← citeshttps://doi.org/10.24963/ijcai.2021/510openalex-snapshot

Hiding Numerical Vectors in Local Private and Shuffled Messages

inferred← citeshttps://doi.org/10.24963/ijcai.2023/722openalex-snapshot

Efficient Convex Optimization Requires Superlinear Memory (Extended Abstract)

inferred← citeshttps://doi.org/10.25148/etd.fidc008988openalex-snapshot

Privacy-aware Security Applications in the Era of Internet of Things

inferred← citeshttps://doi.org/10.29012/jpc.654openalex-snapshot

Differentially Private Ordinary Least Squares

inferred← citeshttps://doi.org/10.29012/jpc.682openalex-snapshot

Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM

inferred← citeshttps://doi.org/10.29012/jpc.811openalex-snapshot

Consistent Spectral Clustering of Network Block Models under Local Differential Privacy

inferred← citeshttps://doi.org/10.3233/faia230420openalex-snapshot

Integrated Private Data Trading Systems for Data Marketplaces

inferred← citeshttps://doi.org/10.32604/cmc.2023.036450openalex-snapshot

A Double-Compensation-Based Federated Learning Scheme for Data Privacy Protection in a Social IoT Scenario

inferred← citeshttps://doi.org/10.32657/10220/48212openalex-snapshot

Differential privacy for survival analysis and user data collection

inferred← citeshttps://doi.org/10.32657/10220/48831openalex-snapshot

Privacy-preserving decentralized detection in sensor networks

inferred← citeshttps://doi.org/10.32657/10356/143188openalex-snapshot

Operationally constrained quickest change detection with multiple post-change distributions

inferred← citeshttps://doi.org/10.33140/jmtcm.03.01.01openalex-snapshot

Augmented Differential Privacy Framework for Data Analytics

inferred← citeshttps://doi.org/10.3389/fdata.2023.1296469openalex-snapshot

TEE-Graph: efficient privacy and ownership protection for cloud-based graph spectral analysis

inferred← citeshttps://doi.org/10.3390/e25010130openalex-snapshot

Locally Differentially Private Heterogeneous Graph Aggregation with Utility Optimization

inferred← citeshttps://doi.org/10.3390/math11204257openalex-snapshot

PPVC: Towards a Personalized Local Differential Privacy-Preserving Scheme for V2G Charging Networks

inferred← citeshttps://doi.org/10.34028/iajit/17/4/3openalex-snapshot

Privacy-Preserving Data Aggregation Framework for Mobile Service Based Multiuser Collaboration

inferred← citeshttps://doi.org/10.48550/arxiv.2011.03396openalex-snapshot

Bayes Security: A Not So Average Metric

inferred← citeshttps://doi.org/10.48550/arxiv.2206.05091openalex-snapshot

Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging

inferred← citeshttps://doi.org/10.48550/arxiv.2302.12559openalex-snapshot

From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning

inferred← citeshttps://doi.org/10.5220/0012322100003648openalex-snapshot

Federated Learning with Differential Privacy and an Untrusted Aggregator

inferred← citeshttps://doi.org/10.54097/8secrg36openalex-snapshot

ES Data Aggregation Scheme based on Personalized Local Differential Privacy

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