117 citations · 266 across the 7 of their papers we have counts for
9 papers
One (Thread) Can Keep a (PRNG) Secret, but not Two
Ehood Porat, Amit Klein, Benny Pinkas
We present a novel, practical attack on the IPv6 Fragment ID generation algorithm of XNU, which is the kernel used by Apple products such as macOS and iOS. This attack exploits a r…
ScionFL: Efficient and Robust Secure Quantized Aggregation
Yaniv Ben-Itzhak, Helen Möllering, Benny Pinkas +7
Secure aggregation is commonly used in federated learning (FL) to alleviate privacy concerns related to the central aggregator seeing all parameter updates in the clear. Unfortunat…
Privacy-Preserving Epidemiological Modeling on Mobile Graphs
Daniel Günther, Marco Holz, Benjamin Judkewitz +4
The latest pandemic COVID-19 brought governments worldwide to use various containment measures to control its spread, such as contact tracing, social distance regulations, and curf…
Fairness in the Eyes of the Data: Certifying Machine-Learning Models
Shahar Segal, Yossi Adi, Benny Pinkas +3
We present a framework that allows to certify the fairness degree of a model based on an interactive and privacy-preserving test. The framework verifies any trained model, regardle…
From IP ID to Device ID and KASLR Bypass (Extended Version)
Amit Klein, Benny Pinkas
IP headers include a 16-bit ID field. Our work examines the generation of this field in Windows (versions 8 and higher), Linux and Android, and shows that the IP ID field enables r…
SBFT: a Scalable and Decentralized Trust Infrastructure
Guy Golan Gueta, Ittai Abraham, Shelly Grossman +6
SBFT is a state of the art Byzantine fault tolerant permissioned blockchain system that addresses the challenges of scalability, decentralization and world-scale geo-replication. S…
Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring
Yossi Adi, Carsten Baum, Moustapha Cisse +2
Deep Neural Networks have recently gained lots of success after enabling several breakthroughs in notoriously challenging problems. Training these networks is computationally expen…
Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples
Felix Kreuk, Assi Barak, Shir Aviv-Reuven +3
In recent years, deep learning has shown performance breakthroughs in many applications, such as image detection, image segmentation, pose estimation, and speech recognition. Howev…
The Circle Game: Scalable Private Membership Test Using Trusted Hardware
Sandeep Tamrakar, Jian Liu, Andrew Paverd +3
Malware checking is changing from being a local service to a cloud-assisted one where users' devices query a cloud server, which hosts a dictionary of malware signatures, to check…