Zobrazeno 1 - 10
of 286
pro vyhledávání: '"Polychroniadou, A."'
Our work aims to minimize interaction in secure computation due to the high cost and challenges associated with communication rounds, particularly in scenarios with many clients. In this work, we revisit the problem of secure aggregation in the singl
Externí odkaz:
http://arxiv.org/abs/2410.22303
Federated Learning (FL) has gained lots of traction recently, both in industry and academia. In FL, a machine learning model is trained using data from various end-users arranged in committees across several rounds. Since such data can often be sensi
Externí odkaz:
http://arxiv.org/abs/2410.16161
Publikováno v:
AAMAS 2024
Banks publish daily a list of available securities/assets (axe list) to selected clients to help them effectively locate Long (buy) or Short (sell) trades at reduced financing rates. This reduces costs for the bank, as the list aggregates the bank's
Externí odkaz:
http://arxiv.org/abs/2404.06686
Autor:
Lazri, Zachary McBride, Dervovic, Danial, Polychroniadou, Antigoni, Brugere, Ivan, Dachman-Soled, Dana, Wu, Min
Applications that deal with sensitive information may have restrictions placed on the data available to a machine learning (ML) classifier. For example, in some applications, a classifier may not have direct access to sensitive attributes, affecting
Externí odkaz:
http://arxiv.org/abs/2403.07724
Autor:
Zhou, Yvonne, Liang, Mingyu, Brugere, Ivan, Dachman-Soled, Dana, Dervovic, Danial, Polychroniadou, Antigoni, Wu, Min
The growing use of machine learning (ML) has raised concerns that an ML model may reveal private information about an individual who has contributed to the training dataset. To prevent leakage of sensitive data, we consider using differentially-priva
Externí odkaz:
http://arxiv.org/abs/2402.04375
Autor:
Lazri, Zachary McBride, Brugere, Ivan, Tian, Xin, Dachman-Soled, Dana, Polychroniadou, Antigoni, Dervovic, Danial, Wu, Min
Increases in the deployment of machine learning algorithms for applications that deal with sensitive data have brought attention to the issue of fairness in machine learning. Many works have been devoted to applications that require different demogra
Externí odkaz:
http://arxiv.org/abs/2310.15097
Autor:
Polychroniadou, Antigoni, Asharov, Gilad, Diamond, Benjamin, Balch, Tucker, Buehler, Hans, Hua, Richard, Gu, Suwen, Gimler, Greg, Veloso, Manuela
Publikováno v:
Prime match: A privacy-preserving inventory matching system. In Joseph A. Calandrino and Carmela Troncoso, editors, 32nd USENIX Security Symposium, USENIX Security 2023, Anaheim, CA, USA, August 9-11, 2023. USENIX Association, 2023
Inventory matching is a standard mechanism/auction for trading financial stocks by which buyers and sellers can be paired. In the financial world, banks often undertake the task of finding such matches between their clients. The related stocks can be
Externí odkaz:
http://arxiv.org/abs/2310.09621
This paper introduces Flamingo, a system for secure aggregation of data across a large set of clients. In secure aggregation, a server sums up the private inputs of clients and obtains the result without learning anything about the individual inputs
Externí odkaz:
http://arxiv.org/abs/2308.09883
Privacy-preserving federated learning enables a population of distributed clients to jointly learn a shared model while keeping client training data private, even from an untrusted server. Prior works do not provide efficient solutions that protect a
Externí odkaz:
http://arxiv.org/abs/2202.09897
Autor:
Pistoia, Marco, Amer, Omar, Behera, Monik R., Dolphin, Joseph A., Dynes, James F., John, Benny, Haigh, Paul A., Kawakura, Yasushi, Kramer, David H., Lyon, Jeffrey, Moazzami, Navid, Movva, Tulasi D., Polychroniadou, Antigoni, Shetty, Suresh, Sysak, Greg, Toudeh-Fallah, Farzam, Upadhyay, Sudhir, Woodward, Robert I., Shields, Andrew J.
Publikováno v:
Quantum Science and Technology, Institute of Physics, May 2023
This article describes experimental research studies conducted towards understanding the implementation aspects of high-capacity quantum-secured optical channels in mission-critical metro-scale operational environments using Quantum Key Distribution
Externí odkaz:
http://arxiv.org/abs/2202.07764