Cover Image

Learning to Localize Leakage of Cryptographic Sensitive Variables

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Gammell, Jimmy, Raghunathan, Anand, Hashemi, Abolfazl, Roy, Kaushik
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Power side-channel leakage localization through adversarial training of deep neural networks
by: Gammell, Jimmy, et al.
Published: (2024)

Unveiling Privacy, Memorization, and Input Curvature Links
by: Ravikumar, Deepak, et al.
Published: (2024)

A Machine Learning-Based Framework for Assessing Cryptographic Indistinguishability of Lightweight Block Ciphers
by: Dani, Jimmy, et al.
Published: (2024)

Curvature Clues: Decoding Deep Learning Privacy with Input Loss Curvature
by: Ravikumar, Deepak, et al.
Published: (2024)

INTACT: Intent-Aware Representation Learning for Cryptographic Traffic Violation Detection
by: Ray, Rahul D
Published: (2026)

A Deep-Learning Technique to Locate Cryptographic Operations in Side-Channel Traces
by: Chiari, Giuseppe, et al.
Published: (2024)

Cryptographic Backdoor for Neural Networks: Boon and Bane
by: Ngo, Anh Tu, et al.
Published: (2025)

Model Inversion Attacks Meet Cryptographic Fuzzy Extractors
by: Prabhakar, Mallika, et al.
Published: (2025)

Information Leakage from Data Updates in Machine Learning Models
by: Hui, Tian, et al.
Published: (2023)

Unelicitable Backdoors in Language Models via Cryptographic Transformer Circuits
by: Draguns, Andis, et al.
Published: (2024)

Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning
by: Fan, Mingyuan, et al.
Published: (2022)

Random Gradient Masking as a Defensive Measure to Deep Leakage in Federated Learning
by: Kim, Joon, et al.
Published: (2024)

Privacy Leakage via Output Label Space and Differentially Private Continual Learning
by: Tobaben, Marlon, et al.
Published: (2024)

Location Leakage in Federated Signal Maps
by: Bakopoulou, Evita, et al.
Published: (2021)

PrivGNN: High-Performance Secure Inference for Cryptographic Graph Neural Networks
by: Wang, Fuyi, et al.
Published: (2025)

Backdoor Channels Hidden in Latent Space: Cryptographic Undetectability in Modern Neural Networks
by: Eggen, Marte, et al.
Published: (2026)

Beyond Static Tools: Evaluating Large Language Models for Cryptographic Misuse Detection
by: Masood, Zohaib, et al.
Published: (2024)

Building Gradient Bridges: Label Leakage from Restricted Gradient Sharing in Federated Learning
by: Zhang, Rui, et al.
Published: (2024)

A Cryptographic Perspective on Mitigation vs. Detection in Machine Learning
by: Gluch, Greg, et al.
Published: (2025)

SoK: Enhancing Cryptographic Collaborative Learning with Differential Privacy
by: Capano, Francesco, et al.
Published: (2026)

Evaluating Privacy Leakage in Split Learning
by: Qiu, Xinchi, et al.
Published: (2023)

Analysis of Privacy Leakage in Federated Large Language Models
by: Vu, Minh N., et al.
Published: (2024)

Information Leakage from Embedding in Large Language Models
by: Wan, Zhipeng, et al.
Published: (2024)

A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
by: Shao, Jiawei, et al.
Published: (2023)

When AI Defeats Password Deception! A Deep Learning Framework to Distinguish Passwords and Honeywords
by: Dani, Jimmy, et al.
Published: (2024)

User authentication system based on human exhaled breath physics
by: Karunanethy, Mukesh, et al.
Published: (2024)

DCT-CryptoNets: Scaling Private Inference in the Frequency Domain
by: Roy, Arjun, et al.
Published: (2024)

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

Discovering Universal Activation Directions for PII Leakage in Language Models
by: Marchyok, Leo, et al.
Published: (2026)

Pr$εε$mpt: Sanitizing Sensitive Prompts for LLMs
by: Chowdhury, Amrita Roy, et al.
Published: (2025)

Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage
by: Meng, Jiayang, et al.
Published: (2025)

Investigating Privacy Leakage in Dimensionality Reduction Methods via Reconstruction Attack
by: Lumbut, Chayadon, et al.
Published: (2024)

Unveiling Client Privacy Leakage from Public Dataset Usage in Federated Distillation
by: Shi, Haonan, et al.
Published: (2025)

Functional Encryption in Secure Neural Network Training: Data Leakage and Practical Mitigations
by: Ioniţă, Alexandru, et al.
Published: (2025)

Synth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesis
by: Ward, Joshua, et al.
Published: (2025)

When GPT Spills the Tea: Comprehensive Assessment of Knowledge File Leakage in GPTs
by: Shen, Xinyue, et al.
Published: (2025)

Defeating Cerberus: Concept-Guided Privacy-Leakage Mitigation in Multimodal Language Models
by: Zhang, Boyang, et al.
Published: (2025)

DeepLeak: Privacy Enhancing Hardening of Model Explanations Against Membership Leakage
by: Hmida, Firas Ben, et al.
Published: (2026)

Leakage Safe Graph Features for Interpretable Fraud Detection in Temporal Transaction Networks
by: Khaleghpour, Hamideh, et al.
Published: (2026)

Gradient-Free Privacy Leakage in Federated Language Models through Selective Weight Tampering
by: Rashid, Md Rafi Ur, et al.
Published: (2023)