Browsing by Author "Dimitrova, Vesna"

Now showing 1 - 2 of 2

Citation Count: 0
Mathematical Foundations and Implementation of Coniks Key Transparency
(Mdpi, 2024) Mollakuqe, Elissa; Dag, Hasan; Dimitrova, Vesna
This research paper explores the CONIKS key management system's security and efficiency, a system designed to ensure transparency and privacy in cryptographic operations. We conducted a comprehensive analysis of the underlying mathematical principles, focusing on cryptographic hash functions and digital signature schemes, and their implementation in the CONIKS model. Through the use of Merkle trees, we verified the integrity of the system, while zero-knowledge proofs were utilized to ensure the confidentiality of key bindings. We conducted experimental evaluations to measure the performance of cryptographic operations like key generation, signing, and verification with varying key sizes and compared the results against theoretical expectations. Our findings demonstrate that the system performs as predicted by cryptographic theory, with only minor deviations in computational time complexities. The analysis also reveals significant trade-offs between security and efficiency, particularly when larger key sizes are used. These results confirm that the CONIKS system offers a robust framework for secure and efficient key management, highlighting its potential for real-world applications in secure communication systems.
Citation Count: 0
Securing Ai Systems: a Comprehensive Overview of Cryptographic Techniques for Enhanced Confidentiality and Integrity
(Ieee, 2024) Garcia, Jose Luis Cano; Udechukwu, Izuchukwu Patrick; Ibrahim, Isiaq Bolaji; Chukwu, Ikechukwu John; Dag, Hasan; Dimitrova, Vesna; Mollakuqe, Elissa
The rapid evolution of artificial intelligence (AI) has introduced transformative changes across industries, accompanied by escalating security concerns. This paper contributes to the imperative need for robust security measures in AI systems based on the application of cryptographic techniques. This research analyzes AI-ML systems vulnerabilities and associated risks and identifies existing cryptographic methods that could constitute security measures to mitigate such risks. Information assets subject to cyberattacks are identified, such as training data and model parameters, followed by a description of existing encryption algorithms and a suggested approach to use a suitable technique, such as homomorphic encryption CKKS, along with digital signatures based on ECDSA to protect the digital assets through all the AI system life cycle. These methods aim to safeguard sensitive data, algorithms, and AI-generated content from unauthorized access and tampering. The outcome offers potential and practical solutions against privacy breaches, adversarial attacks, and misuse of AI-generated content. Ultimately, this work aspires to bolster public trust in AI technologies, fostering innovation in a secure and reliable AI-driven landscape.