Susilo / Pieprzyk Information Security and Privacy

30th Australasian Conference, ACISP 2025, Wollongong, NSW, Australia, July 14–16, 2025, Proceedings, Part III
Erscheinungsjahr 2025
ISBN: 978-981-969101-2
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 484 Seiten

Reihe: Lecture Notes in Computer Science

This three-volume set in LNCS constitutes the refereed proceedings of the 30th Australasian Conference on Information Security and Privacy, ACISP 2025, held in Wollongong, NSW, Australia, during July 14–16, 2025. The 54 full papers, 6 short papers and 1 invited paper included in this book were carefully reviewed and
selected from 181 submissions. They were organized in topical sections as follows: symmetric-key cryptography and cryptanalysis; public-key encryption; digital signatures and zero knowledge; cryptographic protocols and blockchain; post-quantum cryptography; homomorphic encryption and applications; cryptographic foundations and number theory; privacy enhancing technologies; AI security and privacy; system security.

Zielgruppe

Research

Autoren/Hrsg.

Susilo, Willy

Pieprzyk, Josef

Inhaltsverzeichnis

.- Privacy Enhancing Technologies.

.- Comparing and Improving Frequency Estimation Perturbation Mechanisms under Local Differential Privacy.

.- Strong Federated Authentication With Password-based Credential Against Identity Server Corruption.

.- Anonymous Credentials with Credential Redaction and Its Application to SSI-based Plug Charge for Shared Vehicles.

.- Direction-Oriented Smooth Sensitivity and Its Application to Genomic Statistical Analysis.

.- Sentence Embedding Generation Method for Differential Privacy Protection.

.-KD-IBMRKE-PPFL: A Privacy-Preserving Federated Learning Framework Integrating Knowledge Distillation and Identity-Based Multi-Receiver Key
Encapsulation.

.- AI Security and Privacy.

.- Identifying the Truth of Global Model: A Generic Solution to Defend Against Byzantine and Backdoor Attacks in Federated Learning.

.-RAGLeak: Membership Inference Attacks on RAG-Based Large Language
Models.

.- DeGain: Detecting GAN-based Data Inversion in Collaborative Deep Learning.

.- FRFL: Fair and Robust Federated Learning Incentive Model Based on Game Theory.

.- DPFedSub: A Differentially Private Federated Learning with Randomized Subspace Descend.

.- MG-Det: Deepfake Detection with Multi-Granularity.

.- LPIA: Label Preference Inference Attack against Federated Graph Learning.

.- DARA: Enhancing Vulnerability Alignment via Adaptive Reconstruction and Dual-Level Attention.

.- Zeroth-Order Federated Private Tuning for Pretrained Large Language Models.

.- Understanding the Robustness of Machine-Unlearning Models.

.- System Security.

.- Mitigating the Unprivileged User Namespaces based Privilege Escalation Attacks with Linux Capabilities.

.- SoK: From Systematization to Best Practices in Fuzz Driver Generation.

.- Facial Authentication Security Evaluation against Deepfake Attacks in Mobile Apps.

.- Short Papers.

.- EAPIR: Efficient and Authenticated Private Information Retrieval with Fast Server Processing.

.- Ransomware Encryption Detection: Adaptive File System Analysis Against Evasive Encryption Tactics.

.- Receiver-initiated Updatable Public Key Encryption: Construction, Security and Application.

.- Robust and Privacy-Preserving Dynamic Average Consensus with Individual Weight.

.- Improving RSA Cryptanalysis: Combining Continued Fractions and Coppersmith’s Techniques.

.- Shortest Printable Shellcode Encoding Algorithm Based on Dynamic Bitwidth Selection.

.- Position Paper.

.- Bridging Clone Detection and Industrial Compliance: A Practical Pipeline for Enterprise Codebases.