Batch Fully Homomorphic Encryption over the Integers

By Jung Hee Cheon, Jean-Sébastien Coron, Jinsu Kim, M. Lee, Tancrède Lepoint, Mehdi Tibouchi, Aaram Yun. International Conference on the Theory and Application of Cryptographic Techniques, 2013

376 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/ecfafdcafbda2018efefddfd1a824058fc81d377

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Citations in the corpus

Encrypted data processing with Homomorphic Re-Encryption (2017, methodology): “designed an extension of FHE [12] to support the encryption and processing over a vector of plaintext bits rather than a single plaintext bit to generate a ciphertext.”
Accelerating Fully Homomorphic Encryption over the Integers with Super-size Hardware Multiplier and Modular Reduction (2013, methodology): “Recently research improvements in this area such as the batch FHE over the integers [26] confirm that this potential indeed exists for both hardware architecture and implementation developments.”
Accelerating LTV Based Homomorphic Encryption in Reconfigurable Hardware (2015, methodology): “Despite the rapid progression of new FHE optimization techniques such as ones developed to render expensive bootstrapping evaluations obsolete [5] and ones for more effective parallel processing through batching of multiple data bits into a ciphertext [4,9,33], FHE is still far from being ready for ”
Accelerating Somewhat Homomorphic Evaluation using FPGAs (2015, methodology): “Despite the rapid progression of new FHE optimization techniques such as ones developed to render expensive bootstrapping evaluations obsolete [5] and ones for more effective parallel processing through batching of multiple data bits into a ciphertext [33, 4, 9], FHE is still far from being ready fo”
Bootstrapping for Approximate Homomorphic Encryption (2018, background): “There have been a number of studies [22, 8, 9, 6, 7, 25, 30, 11, 5, 31, 19, 16] to improve the eﬃciency of HE cryptosystem after Gentry’s blueprint [26].”
Privacy-Preserving Data Processing with Flexible Access Control (2020, positive): “Due to high computation overhead, some extended schemes [29], [30] were proposed to improve FHE efﬁciency.”
A High-Speed Accelerator for Homomorphic Encryption using the Karatsuba Algorithm (2017, methodology): “With batching, several messages are “packed” within one ciphertext using the Chinese Remainder Theorem (CRT) [12].”
Improving TFHE: faster packed homomorphic operations and efficient circuit bootstrapping (2017, methodology): “But as in [2, 10, 15], our technique relies on a”
A Survey on Homomorphic Encryption Schemes (2017, methodology): “2013] (merged version of [Coron et al. 2013] and [Kim et al.”
Accelerating integer-based fully homomorphic encryption using Comba multiplication (2014, methodology): “Moreover, the designs proposed include building blocks, such as large integer multiplication, which are integral to most FHE schemes, such as Gentry and Halevi’s FHE scheme [5].”
A Custom Accelerator for Homomorphic Encryption Applications (2017, methodology): “data bits into a ciphertext [5], [6], [7], it was not possible to”
High-Speed Fully Homomorphic Encryption Over the Integers (2014, positive): “Moreover, the introduction of a batched FHE scheme over the integers promises further efficiency improvements [21].”