Improved Security for a Ring-Based Fully Homomorphic Encryption Scheme

By Joppe W. Bos, Kristin E. Lauter, Jake Loftus, Michael Naehrig. 2013

476 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

HEAX: An Architecture for Computing on Encrypted Data (2019, background): “Several works [19, 20, 23, 27, 49, 50] focus on improving the performance of YASHE [10] and LTV [45] schemes or their variants.”
A Survey on Homomorphic Encryption Schemes (2017, methodology): “2011; Bos et al. 2013; Lepoint and Naehrig 2014; Rohloff and Cousins 2014). The comparisons for these small-depth FHE implementations are given at Table 4. Since the performance of the state of the art was unsatisfactory, as an early attempt, a relatively simpler FHE, which allows only a few homomor”
Private Predictive Analysis on Encrypted Medical Data (2014, methodology): “Both sets provide 80 bits of security, which means that the algorithms to break the scheme that are considered in [26] take time at least 2(80).”
HEAX: High-Performance Architecture for Computation on Homomorphically Encrypted Data in the Cloud (2019, background): “Several works [58, 24, 27, 59, 20, 21] focus on improving the performance of YASHE [9] and LTV [53] schemes or their variants.”
Shredder: Learning Noise Distributions to Protect Inference Privacy (2019, background): “As Figure 1 illustrates, researchers have attempted to grapplewith this problemby employing cryptographic techniques such as multiparty execution [16, 17] and homomorphic encryption [18–21] in the context of DNNs.”
Fast Private Set Intersection from Homomorphic Encryption (2017, background): “[8, 12, 13, 21, 28, 41]), bringing practical applications close to reality [26, 29, 45].”
Simple Encrypted Arithmetic Library - SEAL v2.1 (2016, methodology): “The previous versions of SEAL use the YASHE’ encryption scheme, introduced in [3].”
Secure Outsourced Matrix Computation and Application to Neural Networks (2018, unknown): “HE is a cryptographic primitive that allows us to compute on encrypted data without decryption and generate an encrypted result which matches that of operations on plaintext [6, 9, 14, 19].”
FPGA-Based High-Performance Parallel Architecture for Homomorphic Computing on Encrypted Data (2019, methodology): “P¨oppelmann et al. [14] implemented the YASHE [8] scheme in the Catapult [34] architecture which is an FPGA-based domain speciﬁc accelerator for cloud computing applications.”
Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference (2020, background): “Since its discovery, many algorithmic improvements have been made to improve performance [6], [8], [9], [22], [25]–[27].”
Private genome analysis through homomorphic encryption (2015, methodology): “We also adapt these methods to the practical HE schemes − BGV scheme [18] by Gentry, Halevi and Smart and YASHE scheme [19] by Bos, Lauter, Loftus and Naehrig.”
Modular Hardware Architecture for Somewhat Homomorphic Function Evaluation (2015, methodology): “The YASHE scheme was introduced by Bos et al. in [6] in 2013.”
Privacy-Preserving Classification on Deep Neural Network (2017, background): “Thanks to the low degree polynomial activation function, the multiplicative depth of the neural network is reasonable during the secure classification, and thus the homomorphic classification with YASHE encryption scheme [6] is efficient.”
Homomorphic Encryption (2017, methodology): “In fact, using the packing technique yields better results for the “second-generation schemes” such as those in [17, 14, 68, 13] than for the “third-generation” GSW scheme [48].”