On-the-fly multiparty computation on the cloud via multikey fully homomorphic encryption
By Adriana López-Alt, Eran Tromer, Vinod Vaikuntanathan. 2012
939 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
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
In Computational Theory and Mathematics
Citations in the corpus
- Secure Single-Server Aggregation with (Poly)Logarithmic Overhead (2020, background): “While there have been both theoretical and appliedworks proposing secure computation solutions for settings with restricted communication [10, 14, 22, 26, 30, 33], Bonawitz et al.”
- HEAX: An Architecture for Computing on Encrypted Data (2019, methodology): “Several works [19, 20, 23, 27, 49, 50] focus on improving the performance of YASHE [10] and LTV [45] schemes or their variants.”
- Crypto-Nets: Neural Networks over Encrypted Data (2014, background): “L´opez-Alt et al. (2012) presented a fully homomorphic encryption scheme that allows joint computation over data that was encrypted with different keys.”
- Efficient Private Statistics with Succinct Sketches (2015, methodology): “Building a cloud-based model from multiple user datasets has been also addressed in [41], which explores the feasibility of Fully Homomorphic Encryption (FHE) based techniques, however, at the moment, FHE operations are still prohibitively expensive.”
- A Survey on Homomorphic Encryption Schemes (2017, methodology): “Even though there exist a few FHE-based MPC protocols (Damgård et al. 2012; López-Alt et al. 2012; Choudhury et al. 2013; Damgård et al. 2016) proposing these powerful and useful tools, unfortunately, their performances are not yet comparable with the conventional MPC approaches (Mood et al.”
- Accelerating Homomorphic Evaluation on Reconfigurable Hardware (2015, methodology): “Examples of SHE schemes are the BGV [8] and LTV [30] schemes and the subsequent YASHE [4] scheme, which are relatively straightforward and conceptually simple as they mainly require polynomial multiplication and (bit level) manipulation of polynomial coefficients for evaluation of ciphertexts (i.e.,”
- Accelerating NTRU based homomorphic encryption using GPUs (2014, methodology): “We use a customized version of the ATV-FHE Scheme that is proposed in [10] by Doröz, Hu and Sunar (DHS).”
- Lattice Signatures and Bimodal Gaussians (2013, methodology): “We then explain how for certain instantiations, this is as hard a problem as Ring-SIS (using the results of Stehlé, Steinfeld [32]) and how for more efficient instantiations, it is a weaker assumption than the ones underlying the classic NTRU encryption scheme and the recent construction of fully-ho”
- Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network Inference (2019, methodology): “Among the techniques for secure computation, Multi-Party Computation (MPC) and Ho-momorphic Encryption (HE) have received increasing attention due to technical breakthroughs.”
- CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy (2016, methodology): “In this work, in particular, we use the homomorphic encryption scheme of Bos et al. (2013), which is very closely related to the schemes in López-Alt et al. (2012); Stehlé & Steinfeld (2011).”
- HEAX: High-Performance Architecture for Computation on Homomorphically Encrypted Data in the Cloud (2019, methodology): “Several works [58, 24, 27, 59, 20, 21] focus on improving the performance of YASHE [9] and LTV [53] schemes or their variants.”
- cuHE: A Homomorphic Encryption Accelerator Library (2015, methodology): “In this section we briefly explain the LTV SWHE [28] with specializations introduced in [11].”