Copertina

Property-Preserving Hashing for $\ell_1$-Distance Predicates: Applications to Countering Adversarial Input Attacks

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Asghar, Hassan, Zhang, Chenhan, Kaafar, Dali
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Cryptography and Security Information Theory Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Preempting Text Sanitization Utility in Resource-Constrained Privacy-Preserving LLM Interactions
di: Carpentier, Robin, et al.
Pubblicazione: (2024)

Practical, Private Assurance of the Value of Collaboration via Fully Homomorphic Encryption
di: Asghar, Hassan Jameel, et al.
Pubblicazione: (2023)

On the Robustness of Malware Detectors to Adversarial Samples
di: Salman, Muhammad, et al.
Pubblicazione: (2024)

Generalization Properties of Adversarial Training for $\ell_0$-Bounded Adversarial Attacks
di: Delgosha, Payam, et al.
Pubblicazione: (2024)

CTIGuardian: A Few-Shot Framework for Mitigating Privacy Leakage in Fine-Tuned LLMs
di: Arachchige, Shashie Dilhara Batan, et al.
Pubblicazione: (2025)

A Large-Scale Empirical Analysis of Custom GPTs' Vulnerabilities in the OpenAI Ecosystem
di: Ogundoyin, Sunday Oyinlola, et al.
Pubblicazione: (2025)

Adversary-Aware Private Inference over Wireless Channels
di: Seif, Mohamed, et al.
Pubblicazione: (2025)

On Polynomial Approximations for Privacy-Preserving and Verifiable ReLU Networks
di: Ali, Ramy E., et al.
Pubblicazione: (2020)

Privacy Against Agnostic Inference Attacks in Vertical Federated Learning
di: Varasteh, Morteza
Pubblicazione: (2023)

Diffusion-aided Task-oriented Semantic Communications with Model Inversion Attack
di: Wang, Xuesong, et al.
Pubblicazione: (2025)

Inference Privacy: Properties and Mechanisms
di: Tian, Fengwei, et al.
Pubblicazione: (2024)

Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity
di: Pour, Alireza F., et al.
Pubblicazione: (2023)

Multi-hop Deep Joint Source-Channel Coding with Deep Hash Distillation for Semantically Aligned Image Recovery
di: Bergström, Didrik, et al.
Pubblicazione: (2025)

Adversarial Attacks to Multi-Modal Models
di: Dou, Zhihao, et al.
Pubblicazione: (2024)

In-Application Defense Against Evasive Web Scans through Behavioral Analysis
di: Ousat, Behzad, et al.
Pubblicazione: (2024)

$d_X$-Privacy for Text and the Curse of Dimensionality
di: Asghar, Hassan Jameel, et al.
Pubblicazione: (2024)

HGAttack: Transferable Heterogeneous Graph Adversarial Attack
di: Zhao, He, et al.
Pubblicazione: (2024)

Multi-granular Adversarial Attacks against Black-box Neural Ranking Models
di: Liu, Yu-An, et al.
Pubblicazione: (2024)

Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training
di: Huang, Alyssa, et al.
Pubblicazione: (2023)

Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments
di: Zhang, Xinwei, et al.
Pubblicazione: (2022)

Adversarial Robustness of Distilled and Pruned Deep Learning-based Wireless Classifiers
di: Baishya, Nayan Moni, et al.
Pubblicazione: (2024)

Joint Interference Detection and Identification via Adversarial Multi-task Learning
di: Xu, H., et al.
Pubblicazione: (2026)

On the Impact of Uncertainty and Calibration on Likelihood-Ratio Membership Inference Attacks
di: Zhu, Meiyi, et al.
Pubblicazione: (2024)

Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy
di: Jin, Richeng, et al.
Pubblicazione: (2023)

Privacy-Preserving Semantic Communications via Multi-Task Learning and Adversarial Perturbations
di: Sagduyu, Yalin E., et al.
Pubblicazione: (2025)

Backdoor Attack against One-Class Sequential Anomaly Detection Models
di: Cheng, He, et al.
Pubblicazione: (2024)

Runtime Detection of Adversarial Attacks in AI Accelerators Using Performance Counters
di: Rahaman, Habibur, et al.
Pubblicazione: (2025)

Adversarial Prompt Evaluation: Systematic Benchmarking of Guardrails Against Prompt Input Attacks on LLMs
di: Zizzo, Giulio, et al.
Pubblicazione: (2025)

Rao Differential Privacy
di: Soto, Carlos
Pubblicazione: (2025)

GeoClip: Geometry-Aware Clipping for Differentially Private SGD
di: Gilani, Atefeh, et al.
Pubblicazione: (2025)

Comparing privacy notions for protection against reconstruction attacks in machine learning
di: Biswas, Sayan, et al.
Pubblicazione: (2025)

Distributional Information Embedding: A Framework for Multi-bit Watermarking
di: He, Haiyun, et al.
Pubblicazione: (2025)

Cryptanalysis via Machine Learning Based Information Theoretic Metrics
di: Kim, Benjamin D., et al.
Pubblicazione: (2025)

Image selective encryption analysis using mutual information in CNN based embedding space
di: Messadi, Ikram, et al.
Pubblicazione: (2025)

Differentially Private Fair Binary Classifications
di: Ghoukasian, Hrad, et al.
Pubblicazione: (2024)

Towards Better Statistical Understanding of Watermarking LLMs
di: Cai, Zhongze, et al.
Pubblicazione: (2024)

Optimal Guarantees for Auditing Rényi Differentially Private Machine Learning
di: Kim, Benjamin D., et al.
Pubblicazione: (2026)

STAMP: Selective Task-Aware Mechanism for Text Privacy
di: Tian, Fengwei, et al.
Pubblicazione: (2026)

The importance of feature preprocessing for differentially private linear optimization
di: Sun, Ziteng, et al.
Pubblicazione: (2023)

Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation
di: Vithana, Sajani, et al.
Pubblicazione: (2024)