Survey on Fully Homomorphic Encryption, Theory, and Applications

Chiara Marcolla; Victor Sucasas; Marc Manzano; Riccardo Bassoli; Frank H.P. Fitzek; Najwa Aaraj

Paper 2022/1602

Survey on Fully Homomorphic Encryption, Theory, and Applications

Chiara Marcolla

, Technology Innovation Institute

Victor Sucasas

, Technology Innovation Institute

Marc Manzano, Sandbox Quantum

Riccardo Bassoli, Institute of Communication Technology, Faculty of Electrical and Computer Engineering, Technische Universität Dresden

Frank H.P. Fitzek, Institute of Communication Technology, Faculty of Electrical and Computer Engineering, Technische Universität Dresden

Najwa Aaraj, Technology Innovation Institute

Abstract

Data privacy concerns are increasing significantly in the context of Internet of Things, cloud services, edge computing, artificial intelligence applications, and other applications enabled by next generation networks. Homomorphic Encryption addresses privacy challenges by enabling multiple operations to be performed on encrypted messages without decryption. This paper comprehensively addresses homomorphic encryption from both theoretical and practical perspectives. The paper delves into the mathematical foundations required to understand fully homomorphic encryption (FHE). It consequently covers design fundamentals and security properties of FHE and describes the main FHE schemes based on various mathematical problems. On a more practical level, the paper presents a view on privacy-preserving Machine Learning using homomorphic encryption, then surveys FHE at length from an engineering angle, covering the potential application of FHE in fog computing, and cloud computing services. It also provides a comprehensive analysis of existing state-of-the-art FHE libraries and tools, implemented in software and hardware, and the performance thereof.

Metadata

Available format(s): PDF
Category: Foundations
Publication info: Published elsewhere. Proceedings of the IEEE
DOI: 10.1109/JPROC.2022.3205665
Keywords: Fully Homomorphic Encryption Homomorphic Encryption Lattices Neural Networks Fog Computing Cloud Computing IoT
Contact author(s): chiara marcolla @ tii ae
victor sucasas @ tii ae
marc @ sandboxquantum com
riccardo bassoli @ tu-dresden de
frank fitzek @ tu-dresden de
najwa aaraj @ tii ae
History: 2022-12-08: revised; 2022-11-17: received; See all versions
Short URL: https://ia.cr/2022/1602
License: CC BY

BibTeX

@misc{cryptoeprint:2022/1602,
      author = {Chiara Marcolla and Victor Sucasas and Marc Manzano and Riccardo Bassoli and Frank H.P. Fitzek and Najwa Aaraj},
      title = {Survey on Fully Homomorphic Encryption, Theory, and Applications},
      howpublished = {Cryptology {ePrint} Archive, Paper 2022/1602},
      year = {2022},
      doi = {10.1109/JPROC.2022.3205665},
      url = {https://eprint.iacr.org/2022/1602}
}