15 citations · 15 across the 6 of their papers we have counts for
7 papers
Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approach
Carlos Cano, Daniel M. Jimenez-Gutierrez, Diego Sal +4
Quantum machine learning (QML) is increasingly recognized as one of the most promising near-term applications of quantum computing, viewed as a next-frontier candidate beyond purel…
Federated Learning for Object Detection: Enabling Collaborative Drone Learning Without Centralizing Data
Daniel M. Jimenez-Gutierrez, Enrique Zuazua, Georgios Kellaris +3
Object detection is a fundamental capability for AI-driven perception in safety-critical drone and edge-vision systems, including disaster response, operational security environmen…
Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Daniel M. Jimenez-Gutierrez, Dario Pighin, Enrique Zuazua +4
Federated Learning (FL) enables collaborative model training among multiple parties without centralizing raw data. There are two main paradigms in FL: Horizontal FL (HFL), where al…
Towards the Next Frontier of LLMs, Training on Private Data: A Cross-Domain Benchmark for Federated Fine-Tuning
Daniel M. Jimenez-Gutierrez, Enrique Zuazua, Georgios Kellaris +3
The recent success of large language models (LLMs) has been largely driven by vast public datasets. However, the next frontier for LLM development lies beyond public data. Much of…
Revealing the Unseen: How to Expose Cloud Usage While Protecting User Privacy
Ata Turk, Mayank Varia, Georgios Kellaris
Cloud users have little visibility into the performance characteristics and utilization of the physical machines underpinning the virtualized cloud resources they use. This uncerta…
: Efficiently Querying Databases While Providing Differential Privacy
Dmytro Bogatov, Georgios Kellaris, George Kollios +2
As organizations struggle with processing vast amounts of information, outsourcing sensitive data to third parties becomes a necessity. To protect the data, various cryptographic t…
Engineering Methods for Differentially Private Histograms: Efficiency Beyond Utility
Georgios Kellaris, Stavros Papadopoulos, Dimitris Papadias
Publishing histograms with -differential privacy has been studied extensively in the literature. Existing schemes aim at maximizing the utility of the published data, while prev…