2 citations · 4 across the 8 of their papers we have counts for
8 papers
Semantic Watermarking with Order-Robust Detection over Sub-sentence Units
Abdulrahman Diaa, Jonathan Petit, Florian Kerschbaum
Semantic watermarks tie the mark to sentence meaning rather than token choices, promising robustness to content-preserving edits. However, the detector only observes attacker-suppl…
ZipPIR: High-throughput Single-server PIR without Client-side Storage
Rasoul Akhavan Mahdavi, Abdulrahman Diaa, Florian Kerschbaum
Private Information Retrieval (PIR) allows a client to privately access a database without revealing which element is accessed. Initial PIR protocols based on Ring Learning with Er…
Optimizing Adaptive Attacks against Watermarks for Language Models
Abdulrahman Diaa, Toluwani Aremu, Nils Lukas
Large Language Models (LLMs) can be misused to spread unwanted content at scale. Content watermarking deters misuse by hiding messages in content, enabling its detection using a se…
FastLloyd: Federated, Accurate, Secure, and Tunable -Means Clustering with Differential Privacy
Abdulrahman Diaa, Thomas Humphries, Florian Kerschbaum
We study the problem of privacy-preserving -means clustering in the horizontally federated setting. Existing federated approaches using secure computation suffer from substantia…
Leveraging Optimization for Adaptive Attacks on Image Watermarks
Nils Lukas, Abdulrahman Diaa, Lucas Fenaux +1
Untrustworthy users can misuse image generators to synthesize high-quality deepfakes and engage in unethical activities. Watermarking deters misuse by marking generated content wit…
Fast and Private Inference of Deep Neural Networks by Co-designing Activation Functions
Abdulrahman Diaa, Lucas Fenaux, Thomas Humphries +9
Machine Learning as a Service (MLaaS) is an increasingly popular design where a company with abundant computing resources trains a deep neural network and offers query access for t…
HE is all you need: Compressing FHE Ciphertexts using Additive HE
Rasoul Akhavan Mahdavi, Abdulrahman Diaa, Florian Kerschbaum
Homomorphic Encryption (HE) is a commonly used tool for building privacy-preserving applications. However, in scenarios with many clients and high-latency networks, communication c…
Privacy-Preserving Federated Recurrent Neural Networks
Sinem Sav, Abdulrahman Diaa, Apostolos Pyrgelis +2
We present RHODE, a novel system that enables privacy-preserving training of and prediction on Recurrent Neural Networks (RNNs) in a cross-silo federated learning setting by relyin…