Domain Obfuscation for Efficient Secure Aggregation of Sparse Feature Vectors

To collaboratively compute the sum of user private feature vectors, secure aggregation protocols are used to preserve the users’ privacy. The existing protocols are expensive in terms of computational/communication costs, making them impractical where the feature vectors are of size in thousands or millions. Also, they do not provide a possibility to define their own privacy and computation budget. In this work, we propose the Domain Obfuscated Secure Aggregation (DO-SAFE) protocol that allows users to define their privacy budget as per their preference. Our theoretical analysis demonstrates that the proposed protocol can significantly reduce communication and computation costs.

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Publication Details

Published
2025-03-12
DOI
https://doi.org/10.1109/icassp49660.2025.10890661
Primary Topic
Domain Adaptation and Few-Shot Learning
Type
article
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article

Domain Obfuscation for Efficient Secure Aggregation of Sparse Feature Vectors

Amit Chaulwar
Domain Adaptation and Few-Shot Learning
article

Domain Obfuscation for Efficient Secure Aggregation of Sparse Feature Vectors

Amit Chaulwar
article en

Abstract

To collaboratively compute the sum of user private feature vectors, secure aggregation protocols are used to preserve the users’ privacy. The existing protocols are expensive in terms of computational/communication costs, making them impractical where the feature vectors are of size in thousands or millions. Also, they do not provide a possibility to define their own privacy and computation budget. In this work, we propose the Domain Obfuscated Secure Aggregation (DO-SAFE) protocol that allows users to define their privacy budget as per their preference. Our theoretical analysis demonstrates that the proposed protocol can significantly reduce communication and computation costs.

Samsung (India) (IN)
Openalex Percentile: Top 98%
Domain Adaptation and Few-Shot Learning
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