6 papers
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…
Backdooring Bias in Large Language Models
Anudeep Das, Prach Chantasantitam, Gurjot Singh +3
Large language models (LLMs) are increasingly deployed in settings where inducing a bias toward a certain topic can have significant consequences, and backdoor attacks can be used…
SilentWood: Private Inference Over Gradient-Boosting Decision Forests
Ronny Ko, Abdelkarim Kati, Robin Geelen +8
Gradient boosting decision forests, used by XGBoost or AdaBoost, offer higher accuracy and lower training times than decision trees for large datasets. Protocols for private infere…
On the Trade-Off Between Transparency and Security in Adversarial Machine Learning
Lucas Fenaux, Christopher Srinivasa, Florian Kerschbaum
Transparency and security are both central to Responsible AI, but they may conflict in adversarial settings. We investigate the strategic effect of transparency for agents through…
ABC: Achieving Better Control of Multimodal Embeddings using VLMs
Benjamin Schneider, Florian Kerschbaum, Wenhu Chen
Visual embedding models excel at zero-shot tasks like visual retrieval and classification. However, these models cannot be used for tasks that contain ambiguity or require user ins…
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…