Non-interactive Multi-client Searchable Symmetric Encryption with Small Client Storage. (arXiv:2212.02859v1 [cs.CR])
Considerable attention has been paid to dynamic searchable symmetric encryption (DSSE) which allows users to search on dynamically updated encrypted databases. To improve the performance of real-world applications, recent non-interactive…
ACRO: A multi-language toolkit for supporting Automated Checking of Research Outputs. (arXiv:2212.02935v1 [cs.CR])
This paper discusses the development of an open source tool ACRO, (Automatic Checking of Research Outputs) to assist researchers and data governance teams by distinguishing between: research output that is…
CySecBERT: A Domain-Adapted Language Model for the Cybersecurity Domain. (arXiv:2212.02974v1 [cs.CR])
The field of cybersecurity is evolving fast. Experts need to be informed about past, current and – in the best case – upcoming threats, because attacks are becoming more advanced,…
On Zero-Knowledge Proofs over the Quantum Internet. (arXiv:2212.03027v1 [quant-ph])
This paper presents a new method for quantum identity authentication (QIA) protocols. The logic of classical zero-knowledge proofs (ZKPs) due to Schnorr is applied in quantum circuits and algorithms. This…
Multiple Perturbation Attack: Attack Pixelwise Under Different $ell_p$-norms For Better Adversarial Performance. (arXiv:2212.03069v1 [cs.CV])
Adversarial machine learning has been both a major concern and a hot topic recently, especially with the ubiquitous use of deep neural networks in the current landscape. Adversarial attacks and…
How to Compare Fuzzers. (arXiv:2212.03075v1 [cs.SE])
Fuzzing is a key method to discover vulnerabilities in programs. Despite considerable progress in this area in the past years, measuring and comparing the effectiveness of fuzzers is still an…
Straggler-Resilient Differentially-Private Decentralized Learning. (arXiv:2212.03080v1 [cs.LG])
We consider the straggler problem in decentralized learning over a logical ring while preserving user data privacy. Especially, we extend the recently proposed framework of differential privacy (DP) amplification by…
How to Compare Fuzzers. (arXiv:2212.03075v1 [cs.SE])
Fuzzing is a key method to discover vulnerabilities in programs. Despite considerable progress in this area in the past years, measuring and comparing the effectiveness of fuzzers is still an…
Multiple Perturbation Attack: Attack Pixelwise Under Different $ell_p$-norms For Better Adversarial Performance. (arXiv:2212.03069v1 [cs.CV])
Adversarial machine learning has been both a major concern and a hot topic recently, especially with the ubiquitous use of deep neural networks in the current landscape. Adversarial attacks and…
Straggler-Resilient Differentially-Private Decentralized Learning. (arXiv:2212.03080v1 [cs.LG])
We consider the straggler problem in decentralized learning over a logical ring while preserving user data privacy. Especially, we extend the recently proposed framework of differential privacy (DP) amplification by…