Knechtel / Chatterjee / Forte Security, Privacy, and Applied Cryptography Engineering

14th International Conference, SPACE 2024, Kottayam, India, December 14–17, 2024, Proceedings
Erscheinungsjahr 2024
ISBN: 978-3-031-80408-3
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 318 Seiten

Reihe: Lecture Notes in Computer Science

This book constitutes the refereed proceedings of the 14th International Conference on Security, Privacy, and Applied Cryptography Engineering, SPACE 2024, held in Kottayam, India, during December 14–17, 2024.

The 8 full papers, 10 short papers and 1 invited paper included in this book were carefully reviewed and selected from 43 submissions. They were organized in topical sections as follows: security, privacy, applied cryptographic engineering, integration of machine learning techniques, reflecting the growing prominence of this approach in contemporary research on security and cryptography, hardware security, the exploration of post-quantum cryptography, and the development of efficient implementations for emerging cryptographic primitives.

Zielgruppe

Research

Autoren/Hrsg.

Knechtel, Johann

Chatterjee, Urbi

Forte, Domenic

Inhaltsverzeichnis

.- Attacks and Countermeasures for Digital Microfluidic Biochips.
.- SideLink: Exposing NVLink to Covert- and Side-Channel Attacks.
.- Faster and more Energy-Efficient Equation Solvers over GF(2).
.- Transferability of Evasion Attacks Against FHE Encrypted Inference.
.- Security Analysis of ASCON Cipher under Persistent Faults.
.- Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering.
.- CoPrIME: Complete Process Isolation using Memory Encryption.
.- Online Testing Entropy and Entropy Tests with a Two State Markov Model.
.- DLShield: A Defense Approach against Dirty Label Attacks in Heterogeneous Federated Learning.
.- Benchmarking Backdoor Attacks on Graph Convolution Neural Networks: A Comprehensive Analysis of Poisoning Techniques.
.- Spatiotemporal Intrusion Detection Systems for IoT Networks.
.- High Speed High Assurance implementations of Mutivariate Quadratic based Signatures.
.- ”There’s always another counter”: Detecting Micro-architectural Attacks in a Probabilistically Interleaved Malicious/Benign Setting.
.- FPGA-Based Acceleration of Homomorphic Convolution with Plaintext Kernels.
.- Post-Quantum Multi-Client Conjunctive Searchable Symmetric Encryption from Isogenies.
.- BlockDoor: Blocking Backdoor Based Watermarks in Deep Neural Networks.
.- Adversarial Malware Detection.
.- ML based Improved Differential Distinguisher with High Accuracy: Application to GIFT-128 and ASCON.