Fischlin / Moonsamy Applied Cryptography and Network Security

23rd International Conference, ACNS 2025, Munich, Germany, June 23–26, 2025, Proceedings, Part III
Erscheinungsjahr 2025
ISBN: 978-3-031-95767-3
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 417 Seiten

Reihe: Lecture Notes in Computer Science

This three-volume set LNCS 15825-15827 constitutes the proceedings of the 23rd International Conference on Applied Cryptography and Network Security, ACNS 2025, held in Munich, Germany, during June 23-26, 2025.
The 55 full papers included in these proceedings were carefully reviewed and selected from 241 submissions. The papers cover all technical aspects of applied cryptography, network and computer security and privacy, representing both academic research work as well as developments in industrial and technical frontiers.

Zielgruppe

Research

Autoren/Hrsg.

Fischlin, Marc

Moonsamy, Veelasha

Inhaltsverzeichnis

Quantum & Post-Quantum: Everlasting Fully Dynamic Group Signatures.- From ElGamal to Lattice-Based: Enhancing the Helios Voting System with Quantum-Safe Cryptography.- Post-Quantum Cryptography for Linux File System Integrity. Biometrics & Authentication: SEBioID: Secure and Effcient Biometric Identification with Two-Party Computation.- SoK: Continuous User Authentication Beyond Error Rates: Reviewing General System Properties.- Cancelable Biometrics based on Cosine Locality Sensitive Hashing and Grouped Inner Product Transformation for Real-valued Features. Privacy: PARSAN-Mix: Packet-Aware Routing and Shuffing with Additional Noise for Latency Optimization in Mix Networks.- ProvDP: Differential Privacy for System Provenance Dataset.- A New Quadratic Noisy Functional Encryption Scheme and Ist Application for Privacy Preserving Machine Learning.- FNSA: An Adaptive Privacy Protection Model for Trajectory Data.- SPPM: A Stackelberg Game-based Personalized Privacy-Preserving Model in Mobile Crowdsensing Systems. Machine Learning: Homomorphic WiSARDs: Effcient Weightless Neural Network training over encrypted data.- LaserGuider: A Laser Based Physical Backdoor Attack against Deep Neural Networks.- Recovering S-Box Design Structures and Quantifying Distances between S-Boxes using Deep Learning.- Obfuscation for Deep Neural Networks against Model-based Extraction Attacks: Taxonomy and Optimization.