Fully Homomorphic Encryption with Relatively Small Key and Ciphertext Sizes

By Nigel P. Smart, Frederik Vercauteren. International Conference on Theory and Practice of Public Key Cryptography, 2010

866 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e2957cbe949d4e76764bec20291a66de89393174

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. Kim Laine
4. Yarkin Doröz
5. Shai Halevi
6. Craig Gentry
7. Vinod Vaikuntanathan
8. Dan Boneh

In Computer Vision and Pattern Recognition

1. Craig Gentry
2. Shai Halevi
3. Vinod Vaikuntanathan
4. Dan Boneh
5. Berk Sunar
6. Wei Dai
7. Zvika Brakerski
8. Frederik Vercauteren

Citations in the corpus

• Homomorphic Signatures for Polynomial Functions (2011, methodology): “While recent groundbreaking work has shown how to compute arbitrary functions on encrypted data [17,33,12], far less is known about computing functions on signed data.”
• UAV-Enabled Intelligent Transportation Systems for the Smart City: Applications and Challenges (2017, methodology): “One promising technique for providing privacy is to utilize the emerging privacy preserving technologies [13, 14] (e.”
• A Custom Accelerator for Homomorphic Encryption Applications (2017, background): “Parallel to these efforts on Gentry scheme, new FHE schemes, suchs as lattice-based [19], [20], [21], integerbased [22], [23], [24] and learning-with-errors (LWE) or (ring) learning with errors ((R)LWE) based encryption [25], [26], [27] schemes, were introduced.”
• Privacy-Preserving Classification on Deep Neural Network (2017, background): “• Schemes based on ideal lattices such as the Gentry’s original work [17] and its optimizations [44], [19].”
• (Leveled) Fully Homomorphic Encryption without Bootstrapping (2014, neutral): “(One could still use R2 as in Smart and Vercauteren [2011], but the number theory there is a bit more involved.”
• Faster Fully Homomorphic Encryption (2010, methodology): “Note that this condition is less restrictive than the corresponding one used in [11, 27, 7] (i.”
• Accelerating Somewhat Homomorphic Evaluation using FPGAs (2015, background): “More specifically, lattice-based [22, 21, 32], integer-based [15, 10, 11] and learning-with-errors (LWE) or (ring) learning with errors ((R)LWE) based encryption [6, 23, 24] schemes were introduced in just a few years.”
• Implementing Gentry's Fully-Homomorphic Encryption Scheme (2011, methodology): “We adopt the Smart-Vercauteren approach [17], in that we also use principal-ideal lattices in the ring of polynomials modulo fn(x) def = xn + 1 with n a power of two.”