A Review of Homomorphic Encryption Libraries for Secure Computation

Sathya, Sai Sri; Vepakomma, Praneeth; Raskar, Ramesh; Ramachandra, Ranjan; Bhattacharya, Santanu

doi:10.48550/arxiv.1812.02428

Cited by 6 publications

(6 citation statements)

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“…, n − 1, where n and p are the polynomial modulus degree and the plaintext coefficient modulus, respectively, as defined in Section II-A. Several methods for encoding numbers into polynomials are available in the literature (e.g., integer encoding and fractional encoding [18]); however, this section focuses on encoding based on binary representation [19], which is a widely used encoding mechanism for natural numbers. The basis of the method is the ability to initially encode m into an n-bit binary representation.…”

Section: B Encoding and Decodingmentioning

confidence: 99%

Privacy-Preserving Deep Learning With Homomorphic Encryption: An Introduction

Falcetta

Roveri

2022

IEEE Comput. Intell. Mag.

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Privacy-preserving deep learning with homomorphic encryption (HE) is a novel and promising research area aimed at designing deep learning solutions that operate while guaranteeing the privacy of user data. Designing privacypreserving deep learning solutions requires one to completely rethink and redesign deep learning models and algorithms to match the severe technological and algorithmic constraints of HE. This paper provides an introduction to this complex research area as well as a methodology for designing privacy-preserving convolutional neural networks (CNNs). This methodology was applied to the design of a privacy-preserving version of the well-known LeNet-1 CNN, which was successfully operated on two benchmark datasets for image classification. Furthermore, this paper details and comments on the research challenges and software resources available for privacy-preserving deep learning with HE.

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Section: B Encoding and Decodingmentioning

confidence: 99%

Privacy-Preserving Deep Learning With Homomorphic Encryption: An Introduction

Falcetta

Roveri

2022

IEEE Comput. Intell. Mag.

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“…Sathya et al [14] compared the Microsoft SEAL, HElib, TFHE, Paillier, ELGamal, and RSA libraries. This review analyzed the features of these libraries for partial and somewhat homomorphic encryption for the various schemes they implement.…”

Section: Work Profiling or Analyzing Other Homomorphic Encryptionmentioning

confidence: 99%

“…Many other works similarly profile FHE performance [8][9][10][11][12][13][14]. In our work, in order to give a fair comparison, we faced the challenge of finding equitable ways to compare libraries while allowing each library the freedom to exercise its parameter selection strategies.…”

Section: Introductionmentioning

confidence: 99%

HEProfiler: An In-Depth Profiler of Approximate Homomorphic Encryption Libraries

Takeshita

Koirala

McKechney

et al. 2022

Preprint

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Fully Homomorphic Encryption (FHE) allows computation on encrypted data. Various software libraries have implemented the approximate-arithmetic FHE scheme CKKS, which is highly useful for applications in machine learning and data analytics; each of these libraries have differing performance and features. It is useful for developers and researchers to learn details about these libraries' performance and their differences. Some previous work has profiled FHE and CKKS implementations for this purpose, but these comparisons are limited in their fairness and completeness. In this article, we compare four major libraries supporting the CKKS scheme. Working with the maintainers of each of the PALISADE, Microsoft SEAL, HElib, and HEAAN libraries, we devise methods for fair comparisons of these libraries, even with their widely varied development strategies and library architectures. To show the practical performance of these libraries, we present HEProfiler, a simple and extensible framework for profiling C++ FHE libraries. Our experimental evaluation is complete in both the scope of tasks tested and metrics evaluated, allowing us to draw conclusions about the behaviors of different libraries under a wide range of real-world workloads. This is the first work giving experimental comparisons of different bootstrapping-capable CKKS libraries.

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“…HE is a promising method to protect both the training and testing procedures; however, such an intensive technique's high computational cost is not tolerable in real-world situations. Several HE schemes in the literature propose alterations and evaluations of the method [10,[91][92][93][94].…”

Section: Homomorphic Encryptionmentioning

confidence: 99%

Privacy and Trust Redefined in Federated Machine Learning

Papadopoulos

Abramson

Hall

et al. 2021

MAKE

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A common privacy issue in traditional machine learning is that data needs to be disclosed for the training procedures. In situations with highly sensitive data such as healthcare records, accessing this information is challenging and often prohibited. Luckily, privacy-preserving technologies have been developed to overcome this hurdle by distributing the computation of the training and ensuring the data privacy to their owners. The distribution of the computation to multiple participating entities introduces new privacy complications and risks. In this paper, we present a privacy-preserving decentralised workflow that facilitates trusted federated learning among participants. Our proof-of-concept defines a trust framework instantiated using decentralised identity technologies being developed under Hyperledger projects Aries/Indy/Ursa. Only entities in possession of Verifiable Credentials issued from the appropriate authorities are able to establish secure, authenticated communication channels authorised to participate in a federated learning workflow related to mental health data.

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A Review of Homomorphic Encryption Libraries for Secure Computation

Cited by 6 publications

References 0 publications

Privacy-Preserving Deep Learning With Homomorphic Encryption: An Introduction

Privacy-Preserving Deep Learning With Homomorphic Encryption: An Introduction

HEProfiler: An In-Depth Profiler of Approximate Homomorphic Encryption Libraries

Privacy and Trust Redefined in Federated Machine Learning

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