Information Security and Privacy : 30th Australasian Conference, ACISP 2025, Wollongong, NSW, Australia, July 14-16, 2025, Proceedings, Part III (Lecture Notes in Computer Science)

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Information Security and Privacy : 30th Australasian Conference, ACISP 2025, Wollongong, NSW, Australia, July 14-16, 2025, Proceedings, Part III (Lecture Notes in Computer Science)

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  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 472 p.
  • 言語 ENG
  • 商品コード 9789819691005

Full Description

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.

Contents

.- 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.

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