Zobrazeno 1 - 10
of 95
pro vyhledávání: '"Pinkas, Benny"'
Autor:
Ben-Itzhak, Yaniv, Möllering, Helen, Pinkas, Benny, Schneider, Thomas, Suresh, Ajith, Tkachenko, Oleksandr, Vargaftik, Shay, Weinert, Christian, Yalame, Hossein, Yanai, Avishay
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. Unfortunately, most existing secure aggregation schemes ignore two critical orth
Externí odkaz:
http://arxiv.org/abs/2210.07376
Autor:
Günther, Daniel, Holz, Marco, Judkewitz, Benjamin, Möllering, Helen, Pinkas, Benny, Schneider, Thomas, Suresh, Ajith
Over the last two years, governments all over the world have used a variety of containment measures to control the spread of COVID-19, such as contact tracing, social distance regulations, and curfews. Epidemiological simulations are commonly used to
Externí odkaz:
http://arxiv.org/abs/2206.00539
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, regardless of its training process and architecture. Thus, it allows us to eva
Externí odkaz:
http://arxiv.org/abs/2009.01534
Autor:
Klein, Amit, Pinkas, Benny
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 remote servers to assign a unique ID to each device and thus be able to
Externí odkaz:
http://arxiv.org/abs/1906.10478
Autor:
Gueta, Guy Golan, Abraham, Ittai, Grossman, Shelly, Malkhi, Dahlia, Pinkas, Benny, Reiter, Michael K., Seredinschi, Dragos-Adrian, Tamir, Orr, Tomescu, Alin
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. SBFTis optimized for decentralization and can easily handle more than 2
Externí odkaz:
http://arxiv.org/abs/1804.01626
Deep Neural Networks have recently gained lots of success after enabling several breakthroughs in notoriously challenging problems. Training these networks is computationally expensive and requires vast amounts of training data. Selling such pre-trai
Externí odkaz:
http://arxiv.org/abs/1802.04633
In recent years, deep learning has shown performance breakthroughs in many applications, such as image detection, image segmentation, pose estimation, and speech recognition. However, this comes with a major concern: deep networks have been found to
Externí odkaz:
http://arxiv.org/abs/1802.04528
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 if particular applications are potentially malware. Whilst such an arc
Externí odkaz:
http://arxiv.org/abs/1606.01655
We propose an efficient framework for enabling secure multi-party numerical computations in a Peer-to-Peer network. This problem arises in a range of applications such as collaborative filtering, distributed computation of trust and reputation, monit
Externí odkaz:
http://arxiv.org/abs/0901.2689
Publikováno v:
The 8th IEEE Peer-to-Peer Computing (P2P 2008), Aachen, Germany, Sept. 2008
We propose an efficient framework for enabling secure multi-party numerical computations in a Peer-to-Peer network. This problem arises in a range of applications such as collaborative filtering, distributed computation of trust and reputation, monit
Externí odkaz:
http://arxiv.org/abs/0810.1624