no code implementations • 12 Jun 2023 • Ghada Almashaqbeh, Zahra Ghodsi
In this paper, we introduce AnoFel, the first framework to support private and anonymous dynamic participation in federated learning.
no code implementations • ICCV 2023 • Zahra Ghodsi, Mojan Javaheripi, Nojan Sheybani, Xinqiao Zhang, Ke Huang, Farinaz Koushanfar
However, keeping the individual updates private allows malicious users to perform Byzantine attacks and degrade the accuracy without being detected.
no code implementations • 17 Jun 2021 • Minsu Cho, Zahra Ghodsi, Brandon Reagen, Siddharth Garg, Chinmay Hegde
The emergence of deep learning has been accompanied by privacy concerns surrounding users' data and service providers' models.
no code implementations • NeurIPS 2021 • Zahra Ghodsi, Nandan Kumar Jha, Brandon Reagen, Siddharth Garg
In this paper we re-think the ReLU computation and propose optimizations for PI tailored to properties of neural networks.
no code implementations • 12 Mar 2021 • Zahra Ghodsi, Siva Kumar Sastry Hari, Iuri Frosio, Timothy Tsai, Alejandro Troccoli, Stephen W. Keckler, Siddharth Garg, Anima Anandkumar
Extracting interesting scenarios from real-world data as well as generating failure cases is important for the development and testing of autonomous systems.
no code implementations • 2 Mar 2021 • Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, Brandon Reagen
This paper proposes DeepReDuce: a set of optimizations for the judicious removal of ReLUs to reduce private inference latency.
no code implementations • NeurIPS 2020 • Zahra Ghodsi, Akshaj Veldanda, Brandon Reagen, Siddharth Garg
Machine learning as a service has given raise to privacy concerns surrounding clients' data and providers' models and has catalyzed research in private inference (PI): methods to process inferences without disclosing inputs.
no code implementations • 11 Feb 2018 • Jeff Zhang, Kartheek Rangineni, Zahra Ghodsi, Siddharth Garg
Hardware accelerators are being increasingly deployed to boost the performance and energy efficiency of deep neural network (DNN) inference.
no code implementations • NeurIPS 2017 • Zahra Ghodsi, Tianyu Gu, Siddharth Garg
Specifically, SafetyNets develops and implements a specialized interactive proof (IP) protocol for verifiable execution of a class of deep neural networks, i. e., those that can be represented as arithmetic circuits.