Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Hayati, Haleh"'
Federated learning (FL) has emerged as a method to preserve privacy in collaborative distributed learning. In FL, clients train AI models directly on their devices rather than sharing data with a centralized server, which can pose privacy risks. Howe
Externí odkaz:
http://arxiv.org/abs/2409.17201
Cloud computing enables users to process and store data remotely on high-performance computers and servers by sharing data over the Internet. However, transferring data to clouds causes unavoidable privacy concerns. Here, we present a synthesis frame
Externí odkaz:
http://arxiv.org/abs/2403.04485
We address the problem of synthesizing distorting mechanisms that maximize infinite horizon privacy for Networked Control Systems (NCSs). We consider stochastic LTI systems where information about the system state is obtained through noisy sensor mea
Externí odkaz:
http://arxiv.org/abs/2303.17519
We present a framework for the design of coding mechanisms that allow remotely operating anomaly detectors in a privacy-preserving manner. We consider the following problem setup. A remote station seeks to identify anomalies based on system input-out
Externí odkaz:
http://arxiv.org/abs/2211.11608
We present a framework for designing distorting mechanisms that allow remotely operating anomaly detectors while preserving privacy. We consider the problem setting in which a remote station seeks to identify anomalies using system input-output signa
Externí odkaz:
http://arxiv.org/abs/2211.03698
Federated learning (FL) has emerged as a privacy solution for collaborative distributed learning where clients train AI models directly on their devices instead of sharing their data with a centralized (potentially adversarial) server. Although FL pr
Externí odkaz:
http://arxiv.org/abs/2204.02497
In this manuscript, we provide a set of tools (in terms of semidefinite programs) to synthesize Gaussian mechanisms to maximize privacy of databases. Information about the database is disclosed through queries requested by (potentially) adversarial u
Externí odkaz:
http://arxiv.org/abs/2111.15307
We address the problem of synthesizing distorting mechanisms that maximize privacy of stochastic dynamical systems. Information about the system state is obtained through sensor measurements. This data is transmitted to a remote station through an un
Externí odkaz:
http://arxiv.org/abs/2108.01755
Publikováno v:
In IFAC PapersOnLine 2023 56(2):11191-11196
Publikováno v:
arXiv, 2021:2108.01755. Cornell University Library
We address the problem of synthesizing distorting mechanisms that maximize privacy of stochastic dynamical systems. Information about the system state is obtained through sensor measurements. This data is transmitted to a remote station through an un
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::18948e6603492c7203167ce25bba2305
https://arxiv.org/abs/2108.01755
https://arxiv.org/abs/2108.01755