1 citations · 1 across the 4 of their papers we have counts for
4 papers
Collaborative CP-NIZKs: Modular, Composable Proofs for Distributed Secrets
Mohammed Alghazwi, Tariq Bontekoe, Leon Visscher +1
Non-interactive zero-knowledge (NIZK) proofs of knowledge have proven to be highly relevant for securely realizing a wide array of applications that rely on both privacy and correc…
Privacy-Preserving, Dropout-Resilient Aggregation in Decentralized Learning
Ali Reza Ghavamipour, Benjamin Zi Hao Zhao, Fatih Turkmen
Decentralized learning (DL) offers a novel paradigm in machine learning by distributing training across clients without central aggregation, enhancing scalability and efficiency. H…
Privacy-Preserving Aggregation for Decentralized Learning with Byzantine-Robustness
Ali Reza Ghavamipour, Benjamin Zi Hao Zhao, Oguzhan Ersoy +1
Decentralized machine learning (DL) has been receiving an increasing interest recently due to the elimination of a single point of failure, present in Federated learning setting. Y…
VPAS: Publicly Verifiable and Privacy-Preserving Aggregate Statistics on Distributed Datasets
Mohammed Alghazwi, Dewi Davies-Batista, Dimka Karastoyanova +1
Aggregate statistics play an important role in extracting meaningful insights from distributed data while preserving privacy. A growing number of application domains, such as healt…