Exploring the Feasibility of Fully Homomorphic Encryption

By Wei Wang, Yin Hu, Lianmu Chen, Xinming Huang, Berk Sunar. 2015

126 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. M. Sadegh Riazi
4. Berk Sunar
5. Blake Pelton
6. Yarkin Doröz
7. Erdinç Öztürk
8. Erkay Savas

In Information Systems

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

Citations in the corpus

• HEAX: An Architecture for Computing on Encrypted Data (2019, methodology): “We evaluate our design on a wide range of FHE parameters: from ciphertext polynomial size ( n ) of 2 12 and 109-bit ciphertext modulus ( ⌊ log qp ⌋ + 1) to 2 14 with 438-bit ciphertext modulus.”
• A Survey on Homomorphic Encryption Schemes (2017, methodology): “In addition to more recently proposed LWE-based FHE schemes in [Zhang et al. 2014; Chen et al. 2014; Tanping et al. 2015; Wang et al. 2015b], some optimizations focused on better (faster) bootstrapping algorithms [Alperin-Sheriff and Peikert 2013; Alperin-Sheriff and Peikert 2014], speeding…”
• SHIELD: Scalable Homomorphic Implementation of Encrypted Data-Classifiers (2015, background): “The same concept can be applied to the secure search application, which is basically • Alhassan Khedr is with the Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada.”
• Accelerating Homomorphic Evaluation on Reconfigurable Hardware (2015, background): “These may be reasons that previous work mainly focuses on using GPUs [19,40,41] and ASICs [21,44], and that FPGA implementations either work only with small parameters and on-chip memory [11] or explicitly do not take into account the complexity of transferring data between an FPGA and a host [9,35]”
• Accelerating leveled fully homomorphic encryption using GPU (2014, methodology): “Later, we managed to keep the calculation in FFT domain as reported in [8] and have about 342 times speedup for encryption, 15 times speedup for recryption and 7 times speedup for decryption.”
• A Custom Accelerator for Homomorphic Encryption Applications (2017, methodology): “This was followed by further FPGA implementations [15], [16], [17], [18].”
• Privacy-Preserving Data Processing with Flexible Access Control (2020, methodology): “Due to high computation overhead, some extended schemes [29], [30] were proposed to improve FHE efficiency.”
• Accelerating NTRU based homomorphic encryption using GPUs (2014, background): “An extension of the authors’ work [26] involves the modification of arithmetic operations to decrease costly back and forth NTT conversions.”
• Accelerating Somewhat Homomorphic Evaluation using FPGAs (2015, background): “This was followed by further investigation in this direction [29, 7, 37, 36].”
• HEAX: High-Performance Architecture for Computation on Homomorphically Encrypted Data in the Cloud (2019, unknown): “Subsequent improvements are reported in [73].”
• Accelerating LTV Based Homomorphic Encryption in Reconfigurable Hardware (2015, background): “This was followed by further investigation in this direction [7,29,36,37].”