Home // SECURWARE 2024, The Eighteenth International Conference on Emerging Security Information, Systems and Technologies // View article
Authors:
Svetlana Boudko
Kristian Teig Grønvold
Keywords: privacy; data security; threshold homomorphic encryption; multi-party computation; distributed analytics.
Abstract:
Distributed analytics, such as federated learning, involve collaborative computation across multiple decentralized devices. This approach not only reduces data transfer costs but also offers some degree of protection for privacy-sensitive information. To achieve a higher level of privacy protection, it is recommended to use more advanced privacy-preserving technologies, such as homomorphic encryption. However, the use of homomorphic encryption schemes results in high computational costs. In this study, we evaluate the performance characteristics of threshold fully homomorphic encryption, a technique that can be effectively applied in multi-user environments and distributed analytics scenarios. We present results from the performance evaluation of the Cheon-Kim-Kim-Song scheme.
Pages: 172 to 175
Copyright: Copyright (c) IARIA, 2024
Publication date: November 3, 2024
Published in: conference
ISSN: 2162-2116
ISBN: 978-1-68558-206-7
Location: Nice, France
Dates: from November 3, 2024 to November 7, 2024