Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation

Sadat, Md. Nazmus; Jiang, Xiaoqian; Aziz, Momin Al; Wang, Shuang; Mohammed, Noman

doi:10.2196/jmi.v6i1.8286

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…• We have conducted several experiments to compare the computation time of the sequential TFHE [1] with our proposed CPU and GPU parallel frameworks. We have also outlined a real-world application with Linear Regression on different datasets [26]. From Table II, our proposed GPU method is 14.4× and 46.81× faster than the existing technique for regular and matrix multiplications, respectively.…”

Section: B Contributionsmentioning

confidence: 99%

CPU and GPU Accelerated Fully Homomorphic Encryption

Morshed¹,

Aziz²,

Mohammed³

2020

Preprint

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Fully Homomorphic Encryption (FHE) is one of the most promising technologies for privacy protection as it allows an arbitrary number of function computations over encrypted data. However, the computational cost of these FHE systems limits their widespread applications. In this paper, our objective is to improve the performance of FHE schemes by designing efficient parallel frameworks. In particular, we choose Torus Fully Homomorphic Encryption (TFHE) [1] as it offers exact results for an infinite number of boolean gate (e.g., AND, XOR) evaluations. We first extend the gate operations to algebraic circuits such as addition, multiplication, and their vector and matrix equivalents. Secondly, we consider the multi-core CPUs to improve the efficiency of both the gate and the arithmetic operations.Finally, we port the TFHE to the Graphics Processing Units (GPU) and device novel optimizations for boolean and arithmetic circuits employing the multitude of cores. We also experimentally analyze both the CPU and GPU parallel frameworks for different numeric representations (16 to 32-bit). Our GPU implementation outperforms the existing technique [1], and it achieves a speedup of 20× for any 32-bit boolean operation and 14.5× for multiplications.

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Section: B Contributionsmentioning

confidence: 99%

CPU and GPU Accelerated Fully Homomorphic Encryption

Morshed¹,

Aziz²,

Mohammed³

2020

Preprint

Self Cite

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“…The idea of combining SGX and homomorphic encryption are proposed in very recent works [70], [71], [72], [73]. In these works, only certain operations are performed homomorphically outside the enclave; Otherwise, the ciphertexts are sent to the enclave for decryption and further computa-tion.…”

Section: Related Workmentioning

confidence: 99%

Toward Scalable Fully Homomorphic Encryption Through Light Trusted Computing Assistance

Wang,

Jiang,

Shen

et al. 2019

Preprint

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It has been a long standing problem to securely outsource computation tasks to an untrusted party with integrity and confidentiality guarantees. While fully homomorphic encryption (FHE) is a promising technique that allows computations performed on the encrypted data, it suffers from a significant slow down to the computation. In this paper we propose a hybrid solution that uses the latest hardware Trusted Execution Environments (TEEs) to assist FHE by moving the bootstrapping step, which is one of the major obstacles in designing practical FHE schemes, to a secured SGX enclave. TEEFHE, the hybrid system we designed, makes it possible for homomorphic computations to be performed on smaller ciphertext and secret key, providing better performance and lower memory consumption. We make an effort to mitigate side channel leakages within SGX by making the memory access patterns totally independent from the secret information. The evaluation shows that TEEFHE effectively improves the software only FHE schemes in terms of both time and space.

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Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation

Cited by 2 publications

References 2 publications

CPU and GPU Accelerated Fully Homomorphic Encryption

CPU and GPU Accelerated Fully Homomorphic Encryption

Toward Scalable Fully Homomorphic Encryption Through Light Trusted Computing Assistance

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