Comparative Analysis of Homomorphic Encryption Algorithms Based on Learning with Errors

Babenko, Mikhail Grigorievitch; Golimblevskaia, Elena; Shiriaev, Egor

doi:10.15514/ispras-2020-32(2)-4

Cited by 6 publications

(2 citation statements)

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“…This advantage of reducing the number of function calls is also reflected in the subsequent homomorphic evaluation. A comparison between the BFV and CKKS shows that CKKS is advantageous in terms of the evaluation time required for realizing stochastic addition and multiplication, although the packing/unpacking (i.e., encoding/decoding) of CKKS takes a little longer due to its unique features such as the usage of floating-point representation [38].…”

Section: Basic Implementation and Comparisonmentioning

confidence: 99%

Homomorphic encryption for stochastic computing

et al. 2022

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Homomorphic encryption (HE) method can be used to realize arithmetic operations on encrypted data. This method, however, is limited owing to its low efficiency in performing certain functions, especially those involving several multiplications. As a solution, this paper proposes a new HE-based secure computation scheme, termed as the HE for stochastic computing (HESC); this scheme can homomorphically evaluate both the stochastic addition and multiplication operations, without any bootstrapping. This HESC scheme is constructed based on additive/multiplicative HE, which only supports homomorphic addition/multiplication, and realizes the homomorphic evaluation of stochastic multiplication. The HESC employs the features of stochastic computing (SC) for homomorphic stochastic operations, where stochastic additions and multiplications are performed using random multiplexing and bit-parallel logic operations, respectively. This paper first presents a basic HESC scheme based on additive/multiplicative HE. It then presents an efficient HESC scheme that utilizes the parallelism of lattice-based cryptography (i.e., plaintext packing and vectorized homomorphic evaluation). A new stochastic addition operation is also introduced in this study, which can be used for the HESC instantiated by lattice-based cryptography. This new stochastic addition operation significantly improves the accuracy of the HESC, albeit with the trade-off of increased ciphertext size. Accordingly, this paper also proposes a technique that can reduce the size of ciphertexts, while maintaining the accuracy of the scheme. The basic performance of the HESC implemented with various HEs is demonstrated, along with its applications in polynomial functions and an oblivious inference with a neural network. Lastly, the results thus obtained indicate that the proposed scheme is more advantageous than the conventional schemes. This paper is concluded with some implications/research directions for HESC from perspectives of cryptography and HE implementations.

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Section: Basic Implementation and Comparisonmentioning

confidence: 99%

Homomorphic encryption for stochastic computing

et al. 2022

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“…We will evaluate the performance of the two most successful FHE schemes: Brakerski/Fan-Vercauteren (BFV) (Fan & Vercauteren, 2012) and Cheon, Kim, Kim, and Son (CKKS) (Cheon et al, 2017), used in TenSEAL (Benaissa et al, 2021 and PyFHEl (Ibarrondo & Viand, 2021) python base libraries that we have considered in this paper. Their main feature is the use of the residue number system (RNS) for performing operations (Babenko et al, 2020). This is done by determining the execution time of the main functions (encoding, encryption, addition/multiplication operations, decryption, and decoding) in the scheme, thereby determining the most productive scheme.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of BFV and CKKs Schemes to Secure IoT Data Using TenSeal and Pyfhel Homomorphic Encryption Libraries

Wiryen,

Vigny,

Joseph

et al. 2023

International Journal of Smart Security Technologies

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Internet of things (IoT) devices and applications are on the rise, generating large amounts of sensitive and confidential data that need to be processed securely. Due to resource constraints, the data generated is often stored and processed in the cloud. The drawback of data cloud storage and processing is the fact that it can be hacked, leaked, or sold by cloud companies. Fully homomorphic encryption (FHE) allows computation on encrypted data using basic mathematical operations and has recently been successfully implemented using schemes and libraries with better performance. In this paper, the authors propose a mixture of edge-cloud-based security schemes using FHE to secure IoT data. The authors evaluate the performance of two FHE schemes (BFV and CKKS) based on data: encoding speed, encryption speed, arithmetic operations (addition and multiplication) speed, and decryption decoding speed using two Python libraries (TenSEAL and PyFHEl). The encryption and decryption are done at the edge node using a Raspberry Pi 4, while the processing is done at the cloud node using a laptop.

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