Implementing Gentry's Fully-Homomorphic Encryption Scheme

By Craig Gentry, Shai Halevi. 2011

1,035 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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. Kim Laine
3. Berk Sunar
4. Shai Halevi
5. Yarkin Doröz
6. Miran Kim
7. Craig Gentry
8. Dan Boneh

In Information Systems

1. Wei Dai
2. Kim Laine
3. Berk Sunar
4. M. Sadegh Riazi
5. Blake Pelton
6. Yarkin Doröz
7. Erdinç Öztürk
8. Erkay Savas

Citations in the corpus

• HEAX: An Architecture for Computing on Encrypted Data (2019, background): “[59] have proposed the first GPU acceleration of FHE that targets Gentry-Halevi [34] scheme.”
• VC3: Trustworthy Data Analytics in the Cloud Using SGX (2015, background): “However, FHE is not efficient for most computations [23], [65].”
• A Survey on Homomorphic Encryption Schemes (2017, methodology): “s a variant of Gentry’s scheme was introduced in [Smart and Vercauteren 2010]. The scheme uses smaller ciphertext and key sizes than Gentry’s scheme without sacrificing the security. Some later works [Gentry and Halevi 2011; Scholl and Smart 2011; Ogura et al. 2010] focused on the optimizations in th”
• (Leveled) fully homomorphic encryption without bootstrapping (2012, neutral): “by evaluating decryption using symmetric polynomials [8, 9] or with a variant of the “grade-school addition” approach used in the Gentry-Halevi implementation [10].”
• Outsourcing the Decryption of ABE Ciphertexts (2011, background): “Gentry and Halevi [22] showed that even for weak security parameters one “bootstrapping” operation of the homomorphic operation would take at least 30 seconds on a high performance machine (and 30 minutes for the high security parameter).”
• Public Key Compression and Modulus Switching for Fully Homomorphic Encryption over the Integers (2012, methodology): “Using some of the optimizations from [10], the authors of [7] report similar performances: a 802 MB public key and a ciphertext refresh in 14 minutes.”
• Exploring the Feasibility of Fully Homomorphic Encryption (2015, methodology): “Table 1 Parameter sizes as proposed by Gentry and Halevi [3]”
• Efficient Fully Homomorphic Encryption from (Standard) LWE (2011, background): “In fact, a recent work of Gentry, Halevi, and Vaikuntanathan [GHV10b] showed that (a slight variant of) this scheme supports just a single homomorphic multiplication, but at the expense of a huge blowup to the ciphertext which made further advance impossible.”
• Accelerating Homomorphic Evaluation on Reconfigurable Hardware (2015, methodology): “[42] present the first GPU implementation of an FHE scheme and provide results for the Gentry-Halevi [24] scheme on an NVIDIA C2050 GPU.”
• Accelerating fully homomorphic encryption using GPU (2012, methodology): “In Section 4, we present the performance evaluation and experimental results.”
• Single-Database Private Information Retrieval from Fully Homomorphic Encryption (2013, methodology): “Recently, Gentry and Halevi implemented their lattice-based FHE scheme [19].”
• Attribute-Based Encryption With Verifiable Outsourced Decryption (2013, background): “[19], [20] use Gentry’s fully homomorphic encryption system [21] as a building block, and thus the overhead in these schemes is currently too large to be practical [22].”
• Secure Face Matching Using Fully Homomorphic Encryption (2018, methodology): “Their system was based on early an FHE scheme [14] that was computationally expensive (template size of 380 MB and 100 secs per match pair) and did not take full advantage of the specific nature of the face matching problem.”
• FHEW: Bootstrapping Homomorphic Encryption in Less Than a Second (2015, background): “Improving the efficiency of Gentry’s scheme has received even more attention [1,2,6,17–21,23], resulting in enhanced asymptotic performance, and some reference implementations and libraries [17,23] that are efficient enough to be run on a personal computer.”
• Accelerating Fully Homomorphic Encryption in Hardware (2015, methodology): “In Table VII, we compare our design with the software implementation of GH-FHE scheme in [16] and the GPU implementation in [14].”
• A Custom Accelerator for Homomorphic Encryption Applications (2017, background): “Much of the development so far has focused on the Gentry-Halevi FHE [19], which intrinsically works with very large integers.”