• Produktbild: Security, Privacy, and Applied Cryptography Engineering
  • Produktbild: Security, Privacy, and Applied Cryptography Engineering
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Security, Privacy, and Applied Cryptography Engineering 14th International Conference, SPACE 2024, Kottayam, India, December 14–17, 2024, Proceedings

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

13.12.2024

Abbildungen

X, 318 p. 105 illus., 79 illus. in color.

Herausgeber

Johann Knechtel + weitere

Verlag

Springer

Seitenzahl

318

Maße (L/B/H)

23,5/15,5/1,8 cm

Gewicht

499 g

Sprache

Englisch

ISBN

978-3-031-80407-6

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

13.12.2024

Abbildungen

X, 318 p. 105 illus., 79 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

318

Maße (L/B/H)

23,5/15,5/1,8 cm

Gewicht

499 g

Sprache

Englisch

ISBN

978-3-031-80407-6

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Security, Privacy, and Applied Cryptography Engineering
  • Produktbild: Security, Privacy, and Applied Cryptography Engineering

  • .- 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.