Copertina

Locally Differentially Private Embedding Models in Distributed Fraud Prevention Systems

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Perez, Iker, Wong, Jason, Skalski, Piotr, Burrell, Stuart, Mortier, Richard, McAuley, Derek, Sutton, David
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Cryptography and Security Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Towards a Foundation Purchasing Model: Pretrained Generative Autoregression on Transaction Sequences
di: Skalski, Piotr, et al.
Pubblicazione: (2024)

Benchmarking Fraud Detectors on Private Graph Data
di: Goldberg, Alexander, et al.
Pubblicazione: (2025)

Differentially Private Empirical Cumulative Distribution Functions
di: Barczewski, Antoine, et al.
Pubblicazione: (2025)

Preserving Target Distributions With Differentially Private Count Mechanisms
di: Kohli, Nitin, et al.
Pubblicazione: (2026)

Local Differential Privacy for Tensors in Distributed Computing Systems
di: Yuan, Yachao, et al.
Pubblicazione: (2025)

Robustness of Locally Differentially Private Graph Analysis Against Poisoning
di: Imola, Jacob, et al.
Pubblicazione: (2022)

AAA: an Adaptive Mechanism for Locally Differential Private Mean Estimation
di: Wei, Fei, et al.
Pubblicazione: (2024)

Locally Differentially Private In-Context Learning
di: Zheng, Chunyan, et al.
Pubblicazione: (2024)

Revealing the True Cost of Locally Differentially Private Protocols: An Auditing Perspective
di: Arcolezi, Héber H., et al.
Pubblicazione: (2023)

Benchmarking Differentially Private Tabular Data Synthesis
di: Chen, Kai, et al.
Pubblicazione: (2025)

Cookie Monster: Efficient On-device Budgeting for Differentially-Private Ad-Measurement Systems
di: Tholoniat, Pierre, et al.
Pubblicazione: (2024)

Locally Differentially Private Distributed Online Learning with Guaranteed Optimality
di: Chen, Ziqin, et al.
Pubblicazione: (2023)

Data Poisoning Attacks to Locally Differentially Private Frequent Itemset Mining Protocols
di: Tong, Wei, et al.
Pubblicazione: (2024)

Distributed HDMM: Scalable, Distributed, Accurate, and Differentially Private Query Workloads without a Trusted Curator
di: Sedimo, Ratang, et al.
Pubblicazione: (2025)

Exactly Minimax-Optimal Locally Differentially Private Sampling
di: Park, Hyun-Young, et al.
Pubblicazione: (2024)

Advances in Differential Privacy and Differentially Private Machine Learning
di: Das, Saswat, et al.
Pubblicazione: (2024)

Improving Count-Mean Sketch as the Leading Locally Differentially Private Frequency Estimator for Large Dictionaries
di: Pan, Mingen
Pubblicazione: (2024)

S-BDT: Distributed Differentially Private Boosted Decision Trees
di: Peinemann, Thorsten, et al.
Pubblicazione: (2023)

Functional Approximation Methods for Differentially Private Distribution Estimation
di: Tao, Ye, et al.
Pubblicazione: (2025)

Revisiting Locally Differentially Private Protocols: Towards Better Trade-offs in Privacy, Utility, and Attack Resistance
di: Arcolezi, Héber H., et al.
Pubblicazione: (2025)

Dobrushin Coefficients of Private Mechanisms Beyond Local Differential Privacy
di: Grosse, Leonhard, et al.
Pubblicazione: (2026)

Utilizing Blockchain and Smart Contracts for Enhanced Fraud Prevention and Minimization in Health Insurance through Multi-Signature Claim Processing
di: Amin, Md Al, et al.
Pubblicazione: (2024)

Distributed Differentially Private Data Analytics via Secure Sketching
di: Burkhardt, Jakob, et al.
Pubblicazione: (2024)

Differentially Private Sampling from Distributions via Wasserstein Projection
di: Takakura, Shokichi, et al.
Pubblicazione: (2026)

DP-RuL: Differentially-Private Rule Learning for Clinical Decision Support Systems
di: Lamp, Josephine, et al.
Pubblicazione: (2024)

Differentially Private Distance Query with Asymmetric Noise
di: Sheng, Weihong, et al.
Pubblicazione: (2025)

Missing Mass for Differentially Private Domain Discovery
di: Dick, Travis, et al.
Pubblicazione: (2026)

Differentially Private aggregate hints in mev-share
di: Passerat-Palmbach, Jonathan, et al.
Pubblicazione: (2025)

Differentially Private Inductive Miner
di: Schulze, Max, et al.
Pubblicazione: (2024)

Differentially Private Explanations for Clusters
di: Gilad, Amir, et al.
Pubblicazione: (2025)

Locally Differentially Private Frequency Estimation via Joint Randomized Response
di: Zheng, Ye, et al.
Pubblicazione: (2025)

DP-BART for Privatized Text Rewriting under Local Differential Privacy
di: Igamberdiev, Timour, et al.
Pubblicazione: (2023)

Data Poisoning Attacks to Locally Differentially Private Range Query Protocols
di: Liao, Ting-Wei, et al.
Pubblicazione: (2025)

ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations
di: Ling, Xinpeng, et al.
Pubblicazione: (2023)

Majority Vote for Distributed Differentially Private Sign Selection
di: Liu, Weidong, et al.
Pubblicazione: (2022)

Differentially Private Nonparametric Confidence Intervals Under Minimal Distributional Assumptions
di: Shoham, Tomer, et al.
Pubblicazione: (2025)

Scalable Differentially Private Sketches under Continual Observation
di: Holland, Rayne
Pubblicazione: (2025)

Correlated Noise Mechanisms for Differentially Private Learning
di: Pillutla, Krishna, et al.
Pubblicazione: (2025)

Differentially Private Stream Processing at Scale
di: Zhang, Bing, et al.
Pubblicazione: (2023)

Cooperative Local Differential Privacy: Securing Time Series Data in Distributed Environments
di: Singh, Bikash Chandra, et al.
Pubblicazione: (2025)