Public Key Compression and Modulus Switching for Fully Homomorphic Encryption over the Integers

By Jean-Sébastien Coron, David Naccache, Mehdi Tibouchi. International Conference on the Theory and Application of Cryptographic Techniques, 2012

299 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/a9e48575c9dfe14c0a44208a8e871c40ba289b39

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Wei Dai
2. Berk Sunar
3. Yarkin Doröz
4. Erdinç Öztürk
5. Erkay Savas
6. Shai Halevi
7. Kim Laine
8. Máire O’neill

In Information Systems

1. Wei Dai
2. Berk Sunar
3. Yarkin Doröz
4. Erdinç Öztürk
5. Erkay Savas
6. Nigel P. Smart
7. Shai Halevi
8. Máire O’neill

Citations in the corpus

• Accelerating Somewhat Homomorphic Evaluation using FPGAs (2015, methodology): “’s FHE schemes [10, 11], BGV-style FHE schemes [5], [22] and NTRU based FHE, e.”
• Accelerating LTV Based Homomorphic Encryption in Reconfigurable Hardware (2015, methodology): “[10,11] were designed and implemented on a Xilinx Virtex-7 FPGA.”
• A Survey on Homomorphic Encryption Schemes (2017, methodology): “Yuanmi Chen and Phong Q. Nguyen [Chen and Nguyen 2012] proposed an algorithm to break the scheme in [Coron et al. 2012], which is faster than exhaustive search.”
• A Custom Accelerator for Homomorphic Encryption Applications (2017, methodology): “[23], [24] were designed and implemented on a Xilinx Virtex-7 FPGA.”
• Homomorphic Evaluation of the AES Circuit (2012, background): “Some of these works were accompanied by implementation [19,10,6,20,14,7], but all the implementations so far were either “proofs of concept” that can compute only one basic operation at a time (at great cost), or special-purpose implementations limited to evaluating very simple functions.”
• Accelerating NTRU based homomorphic encryption using GPUs (2014, background): “Since its inception, three main branches of homomorphic encryption schemes emerged: lattice-based, integer-based [3]–[5] and learning-with-errors (LWE) or (ring) learning with errors ((R)LWE) based encryption [6]–[8].”
• Accelerating Fully Homomorphic Encryption over the Integers with Super-size Hardware Multiplier and Modular Reduction (2013, methodology): “The proposed hardware architecture for the two encryption primitives in CMNT and CNT are plotted in Fig.”
• Accelerating integer-based fully homomorphic encryption using Comba multiplication (2014, methodology): “An extended version of this work is available on the IACR ePrint Archive [20] and includes optimised hardware architectures for the encryption step of the two integer-based FHE schemes [11], [12].”
• High-Speed Fully Homomorphic Encryption Over the Integers (2014, methodology): “[10] further reduced the public key size to no more than 10.”