Cover Image

Smooth Anonymity for Sparse Graphs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Epasto, Alessandro, Esfandiari, Hossein, Mirrokni, Vahab, Medina, Andres Munoz
Format:	Preprint
Published:	2022
Subjects:	Cryptography and Security Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank
by: Epasto, Alessandro, et al.
Published: (2022)

Private Training & Data Generation by Clustering Embeddings
by: Zhou, Felix, et al.
Published: (2025)

Differentially Private Synthetic Data Release for Topics API Outputs
by: Dick, Travis, et al.
Published: (2025)

Retraining with Predicted Hard Labels Provably Increases Model Accuracy
by: Das, Rudrajit, et al.
Published: (2024)

Perturb-and-Project: Differentially Private Similarities and Marginals
by: Cohen-Addad, Vincent, et al.
Published: (2024)

Improving the Variance of Differentially Private Randomized Experiments through Clustering
by: Javanmard, Adel, et al.
Published: (2023)

DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction
by: Zhang, Xinwei, et al.
Published: (2024)

Differentially Private Clustering in Data Streams
by: Epasto, Alessandro, et al.
Published: (2023)

Optimal Approximation -- Smoothness Tradeoffs for Soft-Max Functions
by: Epasto, Alessandro, et al.
Published: (2020)

Differentially Private Optimization for Non-Decomposable Objective Functions
by: Kong, Weiwei, et al.
Published: (2023)

Fantômas: Understanding Face Anonymization Reversibility
by: Todt, Julian, et al.
Published: (2022)

Samplable Anonymous Aggregation for Private Federated Data Analysis
by: Talwar, Kunal, et al.
Published: (2023)

Latent Geometry as a Structural Monitor: Eigenspace Alignment for Anomaly Detection in Anonymity Networks
by: Chhabra, Vaibhav
Published: (2026)

Towards the Anonymization of the Language Modeling
by: Boutet, Antoine, et al.
Published: (2025)

FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning
by: Fereidooni, Hossein, et al.
Published: (2023)

Auditing Privacy Mechanisms via Label Inference Attacks
by: Busa-Fekete, Róbert István, et al.
Published: (2024)

Recent Advances in Malware Detection: Graph Learning and Explainability
by: Shokouhinejad, Hossein, et al.
Published: (2025)

Rethinking Randomized Smoothing from the Perspective of Scalability
by: Kumari, Anupriya, et al.
Published: (2023)

Augment then Smooth: Reconciling Differential Privacy with Certified Robustness
by: Wu, Jiapeng, et al.
Published: (2023)

Certified PEFTSmoothing: Parameter-Efficient Fine-Tuning with Randomized Smoothing
by: Fu, Chengyan, et al.
Published: (2024)

Provably Cost-Sensitive Adversarial Defense via Randomized Smoothing
by: Xin, Yuan, et al.
Published: (2023)

Convergent Privacy Loss of Noisy-SGD without Convexity and Smoothness
by: Chien, Eli, et al.
Published: (2024)

Generating and Detecting Various Types of Fake Image and Audio Content: A Review of Modern Deep Learning Technologies and Tools
by: Dehghani, Arash, et al.
Published: (2025)

De-Anonymization at Scale via Tournament-Style Attribution
by: Zhang, Lirui, et al.
Published: (2026)

Graph Attention Networks with Physical Constraints for Anomaly Detection
by: Homaei, Mohammadhossein, et al.
Published: (2026)

Adaptive Randomized Smoothing: Certified Adversarial Robustness for Multi-Step Defences
by: Lyu, Saiyue, et al.
Published: (2024)

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing
by: Javanmard, Adel, et al.
Published: (2025)

Breaking On-Chip Communication Anonymity using Flow Correlation Attacks
by: Weerasena, Hansika, et al.
Published: (2023)

SmoothGuard: Defending Multimodal Large Language Models with Noise Perturbation and Clustering Aggregation
by: Su, Guangzhi, et al.
Published: (2025)

An Improved Privacy and Utility Analysis of Differentially Private SGD with Bounded Domain and Smooth Losses
by: Liang, Hao, et al.
Published: (2025)

Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries"
by: Schneider, Thomas, et al.
Published: (2024)

VeriX-Anon: A Multi-Layered Framework for Mathematically Verifiable Outsourced Target-Driven Data Anonymization
by: Daga, Miit, et al.
Published: (2026)

Optimal Rates for $O(1)$-Smooth DP-SCO with a Single Epoch and Large Batches
by: Choquette-Choo, Christopher A., et al.
Published: (2024)

Differentially Private Selection using Smooth Sensitivity
by: Chaves, Iago, et al.
Published: (2025)

Provably effective detection of effective data poisoning attacks
by: Gallagher, Jonathan, et al.
Published: (2025)

Learning from Anonymized and Incomplete Tabular Data
by: Lange, Lucas, et al.
Published: (2026)

Differentially Private Sparse Linear Regression with Heavy-tailed Responses
by: Tian, Xizhi, et al.
Published: (2025)

Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees
by: Quijano, Andrew, et al.
Published: (2025)

A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryption
by: Nguyen, Khoa, et al.
Published: (2025)

Intrusion Detection in IoT Networks Using Hyperdimensional Computing: A Case Study on the NSL-KDD Dataset
by: Ghajari, Ghazal, et al.
Published: (2025)