Evaluation of the Complexity of Fully Homomorphic Encryption Schemes in Implementations of Programs

Chakarov, Dimitar; Papazov, Yavor

doi:10.1145/3345252.3345292

Cited by 3 publications

(2 citation statements)

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“…Adding two b bit integers with the Paillier cryptosystem involves modular multiplication with O(b 2 ) complexity. Additions with an RLWE-like scheme involve polynomial additions linear to the number of bits for the given polynomial degree (Chakarov and Papazov 2019). With a specific integer encoding, subtraction becomes trivial expressed as encrypted additions.…”

Section: Basic Operationsmentioning

confidence: 99%

“…However, with each additional multiplications, the ciphertext noise, cipher size, and cost increase. Generally, multiplying two b bit integers with RLWE-like crypto-systems involves homomorphically computing O(b 2 ) AND circuits (Chakarov and Papazov 2019). On the other hand, the AHE scheme requires one of the operands to be unencrypted to realize multiplication expressed as summations.…”

Section: Basic Operationsmentioning

confidence: 99%

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Confidential machine learning on untrusted platforms: a survey

Sharma

Chen

2021

Cybersecur

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With the ever-growing data and the need for developing powerful machine learning models, data owners increasingly depend on various untrusted platforms (e.g., public clouds, edges, and machine learning service providers) for scalable processing or collaborative learning. Thus, sensitive data and models are in danger of unauthorized access, misuse, and privacy compromises. A relatively new body of research confidentially trains machine learning models on protected data to address these concerns. In this survey, we summarize notable studies in this emerging area of research. With a unified framework, we highlight the critical challenges and innovations in outsourcing machine learning confidentially. We focus on the cryptographic approaches for confidential machine learning (CML), primarily on model training, while also covering other directions such as perturbation-based approaches and CML in the hardware-assisted computing environment. The discussion will take a holistic way to consider a rich context of the related threat models, security assumptions, design principles, and associated trade-offs amongst data utility, cost, and confidentiality.

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Section: Basic Operationsmentioning

confidence: 99%

Section: Basic Operationsmentioning

confidence: 99%