10 citations · 26 across the 9 of their papers we have counts for
10 papers
Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuning
Mustafa Safa Ozdayi, Charith Peris, Jack FitzGerald +5
Large Language Models (LLMs) are known to memorize significant portions of their training data. Parts of this memorized content have been shown to be extractable by simply querying…
The Impact of Data Distribution on Fairness and Robustness in Federated Learning
Mustafa Safa Ozdayi, Murat Kantarcioglu
Federated Learning (FL) is a distributed machine learning protocol that allows a set of agents to collaboratively train a model without sharing their datasets. This makes FL partic…
Towards a Two-Tier Hierarchical Infrastructure: An Offline Payment System for Central Bank Digital Currencies
Mihai Christodorescu, Wanyun Catherine Gu, Ranjit Kumaresan +8
Digital payments traditionally rely on online communications with several intermediaries such as banks, payment networks, and payment processors in order to authorize and process p…
Improving Accuracy of Federated Learning in Non-IID Settings
Mustafa Safa Ozdayi, Murat Kantarcioglu, Rishabh Iyer
Federated Learning (FL) is a decentralized machine learning protocol that allows a set of participating agents to collaboratively train a model without sharing their data. This mak…
BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture
Harsh Bimal Desai, Mustafa Safa Ozdayi, Murat Kantarcioglu
Federated Learning (FL) is a distributed, and decentralized machine learning protocol. By executing FL, a set of agents can jointly train a model without sharing their datasets wit…
Secure IoT Data Analytics in Cloud via Intel SGX
Md Shihabul Islam, Mustafa Safa Ozdayi, Latifur Khan +1
The growing adoption of IoT devices in our daily life is engendering a data deluge, mostly private information that needs careful maintenance and secure storage system to ensure da…
Defending against Backdoors in Federated Learning with Robust Learning Rate
Mustafa Safa Ozdayi, Murat Kantarcioglu, Yulia R. Gel
Federated learning (FL) allows a set of agents to collaboratively train a model without sharing their potentially sensitive data. This makes FL suitable for privacy-preserving appl…
Achieving Competitiveness in Online Problems
Mustafa Safa Ozdayi
In the setting of online algorithms, the input is initially not present but rather arrive one-by-one over time and after each input, the algorithm has to make a decision. Depending…
Leveraging Blockchain for Immutable Logging and Querying Across Multiple Sites
Mustafa Safa Ozdayi, Murat Kantarcioglu, Bradley Malin
Blockchain has emerged as a decentralized and distributed framework that enables tamper-resilience and, thus, practical immutability for stored data. This immutability property is…
Generating massive complex networks with hyperbolic geometry faster in practice
Moritz von Looz, Mustafa Özdayi, Sören Laue +1
Generative network models play an important role in algorithm development, scaling studies, network analysis, and realistic system benchmarks for graph data sets. The commonly used…