Copertina

LFFR: Logistic Function For (single-output) Regression

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Chiang, John
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Cryptography and Security
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

LFFR: Logistic Function For (multi-output) Regression
di: Chiang, John
Pubblicazione: (2024)

Privacy-Preserving Logistic Regression Training with A Faster Gradient Variant
di: Chiang, John
Pubblicazione: (2022)

Privacy-Preserving CNN Training with Transfer Learning: Multiclass Logistic Regression
di: Chiang, John
Pubblicazione: (2023)

Privacy-Preserving Logistic Regression Training on Large Datasets
di: Chiang, John
Pubblicazione: (2024)

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods
di: Ullah, Mohammad Arafat, et al.
Pubblicazione: (2024)

Accuracy Improvement in Differentially Private Logistic Regression: A Pre-training Approach
di: Hoseinpour, Mohammad, et al.
Pubblicazione: (2023)

Privacy-Preserving 3-Layer Neural Network Training
di: Chiang, John
Pubblicazione: (2023)

A Study of Secure Algorithms for Vertical Federated Learning: Take Secure Logistic Regression as an Example
di: Wang, Huan-Chih, et al.
Pubblicazione: (2024)

EVA-S2PLoR: Decentralized Secure 2-party Logistic Regression with A Subtly Hadamard Product Protocol (Full Version)
di: Tao, Tianle, et al.
Pubblicazione: (2025)

Differentially Private Log-Location-Scale Regression Using Functional Mechanism
di: Sheng, Jiewen, et al.
Pubblicazione: (2024)

Privacy-aware Gaussian Process Regression
di: Tuo, Rui, et al.
Pubblicazione: (2023)

PRIMO: Private Regression in Multiple Outcomes
di: Neel, Seth
Pubblicazione: (2023)

Attack logics, not outputs: Towards efficient robustification of deep neural networks by falsifying concept-based properties
di: Dankworth, Raik, et al.
Pubblicazione: (2025)

Private Linear Regression with Differential Privacy and PAC Privacy
di: Yang, Hillary, et al.
Pubblicazione: (2024)

Denoising the US Census: Succinct Block Hierarchical Regression
di: Ghazi, Badih, et al.
Pubblicazione: (2026)

Regression-aware Continual Learning for Android Malware Detection
di: Ghiani, Daniele, et al.
Pubblicazione: (2025)

Noise-Aware Differentially Private Regression via Meta-Learning
di: Räisä, Ossi, et al.
Pubblicazione: (2024)

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

Improving ML Attacks on LWE with Data Repetition and Stepwise Regression
di: Alfarano, Alberto, et al.
Pubblicazione: (2026)

Privacy Amplification Through Synthetic Data: Insights from Linear Regression
di: Pierquin, Clément, et al.
Pubblicazione: (2025)

Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference
di: Liu, Terrance, et al.
Pubblicazione: (2025)

INTARG: Informed Real-Time Adversarial Attack Generation for Time-Series Regression
di: Tokgoz, Gamze Kirman, et al.
Pubblicazione: (2026)

One-Shot Federated Ridge Regression: Exact Recovery via Sufficient Statistic Aggregation
di: Alsulaimawi, Zahir
Pubblicazione: (2026)

Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation
di: Brown, Gavin, et al.
Pubblicazione: (2024)

Modulated learning for private and distributed regression with just a single sample per client device
di: Vepakomma, Praneeth, et al.
Pubblicazione: (2026)

Testing Credibility of Public and Private Surveys through the Lens of Regression
di: Basu, Debabrota, et al.
Pubblicazione: (2024)

On the Lack of Robustness of Binary Function Similarity Systems
di: Capozzi, Gianluca, et al.
Pubblicazione: (2024)

IF-GUIDE: Influence Function-Guided Detoxification of LLMs
di: Coalson, Zachary, et al.
Pubblicazione: (2025)

Differentially Private Release and Learning of Threshold Functions
di: Bun, Mark, et al.
Pubblicazione: (2015)

Rethinking PGD Attack: Is Sign Function Necessary?
di: Yang, Junjie, et al.
Pubblicazione: (2023)

Is Function Similarity Over-Engineered? Building a Benchmark
di: Saul, Rebecca, et al.
Pubblicazione: (2024)

Detection of Compromised Functions in a Serverless Cloud Environment
di: Lavi, Danielle, et al.
Pubblicazione: (2024)

A novel reliability attack of Physical Unclonable Functions
di: Li, Gaoxiang, et al.
Pubblicazione: (2024)

Compact: Approximating Complex Activation Functions for Secure Computation
di: Islam, Mazharul, et al.
Pubblicazione: (2023)

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

BLens: Contrastive Captioning of Binary Functions using Ensemble Embedding
di: Benoit, Tristan, et al.
Pubblicazione: (2024)

Detecting Instruction Fine-tuning Attacks using Influence Function
di: Li, Jiawei
Pubblicazione: (2025)

Understanding Deep Gradient Leakage via Inversion Influence Functions
di: Zhang, Haobo, et al.
Pubblicazione: (2023)

Pure Differential Privacy for Functional Summaries with a Laplace-like Process
di: Lin, Haotian, et al.
Pubblicazione: (2023)

Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions
di: Cinà, Antonio Emanuele, et al.
Pubblicazione: (2021)