Private Linear Computation: Algorithms, Performance Limits, and Applications to Machine Learning

This article surveys Private Computation, a foundational framework for achieving privacy across a broad range of applications. Private Computation techniques enable clients to perform computations on data stored at remote servers without revealing sensitive information about the computation, such as...

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Bibliographic Details

Published in:	IEEE BITS the information theory magazine pp. 1 - 12
Main Authors:	Heidarzadeh, Anoosheh, Sprintson, Alex
Format:	Journal Article
Language:	English
Published:	IEEE 14.11.2025
Subjects:	Data privacy Indexes Information theory Machine learning Prediction algorithms Privacy Random variables Servers Upper bound Vectors
ISSN:	2692-4080, 2692-4110
Online Access:	Get full text
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