Data and Applications Security and Privacy XXXVIII (eBook, PDF)
38th Annual IFIP WG 11.3 Conference, DBSec 2024, San Jose, CA, USA, July 15-17, 2024, Proceedings
Redaktion: Ferrara, Anna Lisa; Krishnan, Ram
Alle Infos zum eBook verschenken
Data and Applications Security and Privacy XXXVIII (eBook, PDF)
38th Annual IFIP WG 11.3 Conference, DBSec 2024, San Jose, CA, USA, July 15-17, 2024, Proceedings
Redaktion: Ferrara, Anna Lisa; Krishnan, Ram
- Format: PDF
- Merkliste
- Auf die Merkliste
- Bewerten Bewerten
- Teilen
- Produkt teilen
- Produkterinnerung
- Produkterinnerung
Hier können Sie sich einloggen
Bitte loggen Sie sich zunächst in Ihr Kundenkonto ein oder registrieren Sie sich bei bücher.de, um das eBook-Abo tolino select nutzen zu können.
This book constitutes the proceedings from the 38th Annual IFIP 11.3 Conference on Data and Applications Security and Privacy XXXVIII, DBSec 2024, held in San Jose, CA, USA, during July 15-17, 2024.
The 14 full papers and 6 short papers presented were carefully reviewed and selected from 39 submissions. The papers are organized in the following topical sections: access control; crypto application; privacy; attack; ml attack, vulnerability; security user studies; and differential privacy.
- Geräte: PC
- ohne Kopierschutz
- eBook Hilfe
- Größe: 27.18MB
- Data and Applications Security and Privacy XXXVII (eBook, PDF)73,95 €
- Privacy and Identity Management. Fairness, Accountability, and Transparency in the Age of Big Data (eBook, PDF)62,95 €
- Security and Trust Management (eBook, PDF)40,95 €
- Data and Applications Security and Privacy XXXVI (eBook, PDF)73,95 €
- Privacy and Identity Management. Data for Better Living: AI and Privacy (eBook, PDF)48,95 €
- Security, Privacy and Data Analytics (eBook, PDF)137,95 €
- Detection of Intrusions and Malware, and Vulnerability Assessment (eBook, PDF)53,95 €
-
-
-
The 14 full papers and 6 short papers presented were carefully reviewed and selected from 39 submissions. The papers are organized in the following topical sections: access control; crypto application; privacy; attack; ml attack, vulnerability; security user studies; and differential privacy.
Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.
- Produktdetails
- Verlag: Springer International Publishing
- Seitenzahl: 342
- Erscheinungstermin: 12. Juli 2024
- Englisch
- ISBN-13: 9783031651724
- Artikelnr.: 72244023
- Verlag: Springer International Publishing
- Seitenzahl: 342
- Erscheinungstermin: 12. Juli 2024
- Englisch
- ISBN-13: 9783031651724
- Artikelnr.: 72244023
- Herstellerkennzeichnung Die Herstellerinformationen sind derzeit nicht verfügbar.
.- A Graph-based Framework for ABAC Policy Enforcement and Analysis.
.- Human Digital Twins: Efficient Privacy-Preserving Access Control Through Views Pre-Materialisation.
.- IAM Meets CTI: Make Identity and Access Management ready for Cyber Threat Intelligence.
.- Crypto Application.
.- SmartSSD-Accelerated Cryptographic Shuffling for Enhancing Database Security.
.- Ensuring End-to-End IoT Data Security & Privacy through Cloud-Enhanced Confidential Computing.
.- Towards Atomicity and Composability in Cross-Chain NFTs.
.- A Privacy-Preserving Graph Encryption Scheme Based on Oblivious RAM.
.- Privacy.
.- DT-Anon: Decision Tree Target-Driven Anonymization.
.- Visor: Privacy-preserving Reputation for Decentralized Marketplaces.
.- Attack.
.- Resiliency Analysis of Mission-critical System of Systems Using Formal Methods.
.- Enhancing EV Charging Station Security Using A Multi-dimensional Dataset : CICEVSE2024.
.- Optimal Automated Generation of Playbooks.
.- ML Attack, Vulnerablity.
.- ALERT: A Framework for Efficient Extraction of Attack Techniques from Cyber Threat Intelligence Reports Using Active Learning.
.- VulPrompt: Prompt-based Vulnerability Detection using Few-shot Graph Learning.
.- All Your LLMs Belong To Us: Experiments with a New Extortion Phishing Dataset.
.- Adaptive Image Adversarial Example Detection Based on Class Activation Mapping.
.- Security User Studies.
.- From Play to Profession: A Serious Game to Raise Awareness on Digital Forensics.
.- User Perception of CAPTCHAs: A Comparative Study between University and Internet Users.
.- Differential Privacy.
.- Incentivized Federated Learning with Local Differential Privacy using Permissioned Blockchains.
.- Does Differential Privacy Prevent Backdoor Attacks in Practice?.
.- A Graph-based Framework for ABAC Policy Enforcement and Analysis.
.- Human Digital Twins: Efficient Privacy-Preserving Access Control Through Views Pre-Materialisation.
.- IAM Meets CTI: Make Identity and Access Management ready for Cyber Threat Intelligence.
.- Crypto Application.
.- SmartSSD-Accelerated Cryptographic Shuffling for Enhancing Database Security.
.- Ensuring End-to-End IoT Data Security & Privacy through Cloud-Enhanced Confidential Computing.
.- Towards Atomicity and Composability in Cross-Chain NFTs.
.- A Privacy-Preserving Graph Encryption Scheme Based on Oblivious RAM.
.- Privacy.
.- DT-Anon: Decision Tree Target-Driven Anonymization.
.- Visor: Privacy-preserving Reputation for Decentralized Marketplaces.
.- Attack.
.- Resiliency Analysis of Mission-critical System of Systems Using Formal Methods.
.- Enhancing EV Charging Station Security Using A Multi-dimensional Dataset : CICEVSE2024.
.- Optimal Automated Generation of Playbooks.
.- ML Attack, Vulnerablity.
.- ALERT: A Framework for Efficient Extraction of Attack Techniques from Cyber Threat Intelligence Reports Using Active Learning.
.- VulPrompt: Prompt-based Vulnerability Detection using Few-shot Graph Learning.
.- All Your LLMs Belong To Us: Experiments with a New Extortion Phishing Dataset.
.- Adaptive Image Adversarial Example Detection Based on Class Activation Mapping.
.- Security User Studies.
.- From Play to Profession: A Serious Game to Raise Awareness on Digital Forensics.
.- User Perception of CAPTCHAs: A Comparative Study between University and Internet Users.
.- Differential Privacy.
.- Incentivized Federated Learning with Local Differential Privacy using Permissioned Blockchains.
.- Does Differential Privacy Prevent Backdoor Attacks in Practice?.