Zobrazeno 1 - 10
of 407
pro vyhledávání: '"P. Sajani"'
Publikováno v:
Indian Journal of Plastic Surgery, Vol 54, Iss 02, Pp 130-137 (2021)
Introduction This is a retrospective therapeutic series of eight cases of facial mucormycosis treated over a 15-year period to determine the safety of simultaneous debridement and free-flap reconstruction in facial mucormycosis. Methods Surgical debr
Externí odkaz:
https://doaj.org/article/200b23c9d37c4a298292090c4d0d87ad
Autor:
Oesterling, Alex, Verdun, Claudio Mayrink, Long, Carol Xuan, Glynn, Alexander, Paes, Lucas Monteiro, Vithana, Sajani, Cardone, Martina, Calmon, Flavio P.
Image search and retrieval tasks can perpetuate harmful stereotypes, erase cultural identities, and amplify social disparities. Current approaches to mitigate these representational harms balance the number of retrieved items across population groups
Externí odkaz:
http://arxiv.org/abs/2407.08571
Differentially private distributed mean estimation (DP-DME) is a fundamental building block in privacy-preserving federated learning, where a central server estimates the mean of $d$-dimensional vectors held by $n$ users while ensuring $(\epsilon,\de
Externí odkaz:
http://arxiv.org/abs/2407.03289
We consider the problem of private membership aggregation (PMA), in which a user counts the number of times a certain element is stored in a system of independent parties that store arbitrary sets of elements from a universal alphabet. The parties ar
Externí odkaz:
http://arxiv.org/abs/2309.03872
We consider both the classical and quantum variations of $X$-secure, $E$-eavesdropped and $T$-colluding symmetric private information retrieval (SPIR). This is the first work to study SPIR with $X$-security in classical or quantum variations. We firs
Externí odkaz:
http://arxiv.org/abs/2308.10883
Autor:
Vithana, Sajani, Ulukus, Sennur
In federated submodel learning (FSL), a machine learning model is divided into multiple submodels based on different types of data used for training. Each user involved in the training process only downloads and updates the submodel relevant to the u
Externí odkaz:
http://arxiv.org/abs/2307.06323
Autor:
Vithana, Sajani, Ulukus, Sennur
We introduce the problem of deceptive information retrieval (DIR), in which a user wishes to download a required file out of multiple independent files stored in a system of databases while \emph{deceiving} the databases by making the databases' pred
Externí odkaz:
http://arxiv.org/abs/2307.04727
We consider a special case of $X$-secure $T$-private information retrieval (XSTPIR), where the security requirement is \emph{asymmetric} due to possible missing communication links between the $N$ databases considered in the system. We define the pro
Externí odkaz:
http://arxiv.org/abs/2305.05649
Private information retrieval (PIR) is a privacy setting that allows a user to download a required message from a set of messages stored in a system of databases without revealing the index of the required message to the databases. PIR was introduced
Externí odkaz:
http://arxiv.org/abs/2304.14397
Autor:
Vithana, Sajani, Ulukus, Sennur
In federated learning (FL), a machine learning (ML) model is collectively trained by a large number of users, using their private data in their local devices. With top $r$ sparsification in FL, the users only upload the most significant $r$ fraction
Externí odkaz:
http://arxiv.org/abs/2303.04123