Accelerating NTRUEncrypt for in-browser cryptography utilising graphical processing units and WebGL

One of the challenges encryption faces is it is computationally intensive and therefore slow, it is vital to find faster methods to accelerate modern encryption algorithms to keep performance high whilst also preserving information security. Users often do not want to wait for applications to become responsive, applications on limited devices such as mobiles often compromise security in order to keep execution times quick. Often they use algorithms and key sizes which are not considered cryptographically secure in order to maintain a smooth user experience. Emerging approaches have begun using a devices Graphics Processing Unit (GPU) to offload some of the computational burden from the Central Processing Unit (CPU) in an effort to parallelize and accelerate the encryption algorithms. Programming for a GPU often involves the use of CUDA or OpenCL programming, however these approaches are platform dependant. This research focuses on utilizing a GPU to perform in-browser cryptography using WebGL and JavaScript. This allows any GPU-enabled device capable of launching an OpenGL compatible browser to perform GPU accelerated cryptography. A GPU based implementation of the NTRUEncrypt algorithm was created and tested against a CPU based version on a range of hardware devices with results, challenges and limitations discussed

A Survey on Implementation of Homomorphic Encryption Scheme in Cloud based Medical Analytical System

The privacy of sensitive personal information is more and more important topic as a result of the increased availability of cloud services. These privacy issues arise due to the legitimate concern of a) having a security breach on these cloud servers or b) the leakage of this sensitive information due to an honest but curious individual at the cloud service provider. Standard encryption schemes try to address the ?rst concern by devising encryption schemes that are harder to break, yet they don’t solve the possible misuse of this sensitive data by the cloud service providers. Homomorphic encryption presents a tool that can solve both types of privacy concerns. The clients are given the possibility of encrypting their sensitive information before sending it to the cloud. The cloud will then compute over their encrypted data without the need for the decryption key. By using homomorphic encryption, servers guarantee to the clients that their valuable information to have no problems after being in a difficult situation.

The Proceedings of 14th Australian Information Security Management Conference, 5-6 December 2016, Edith Cowan University, Perth, Australia

The annual Security Congress, run by the Security Research Institute at Edith Cowan University, includes the Australian Information Security and Management Conference. Now in its fourteenth year, the conference remains popular for its diverse content and mixture of technical research and discussion papers. The area of information security and management continues to be varied, as is reflected by the wide variety of subject matter covered by the papers this year. The conference has drawn interest and papers from within Australia and internationally. All submitted papers were subject to a double blind peer review process. Fifteen papers were submitted from Australia and overseas, of which ten were accepted for final presentation and publication. We wish to thank the reviewers for kindly volunteering their time and expertise in support of this event. We would also like to thank the conference committee who have organised yet another successful congress. Events such as this are impossible without the tireless efforts of such people in reviewing and editing the conference papers, and assisting with the planning, organisation and execution of the conferences. To our sponsors also a vote of thanks for both the financial and moral support provided to the conference. Finally, thank you to the administrative and technical staff, and students of the ECU Security Research Institute for their contributions to the running of the conference

Efficient Computation and FPGA implementation of Fully Homomorphic Encryption with Cloud Computing Significance

Homomorphic Encryption provides unique security solution for cloud computing. It ensures not only that data in cloud have confidentiality but also that data processing by cloud server does not compromise data privacy. The Fully Homomorphic Encryption (FHE) scheme proposed by Lopez-Alt, Tromer, and Vaikuntanathan (LTV), also known as NTRU(Nth degree truncated polynomial ring) based method, is considered one of the most important FHE methods suitable for practical implementation. In this thesis, an efficient algorithm and architecture for LTV Fully Homomorphic Encryption is proposed. Conventional linear feedback shift register (LFSR) structure is expanded and modified for performing the truncated polynomial ring multiplication in LTV scheme in parallel. Novel and efficient modular multiplier, modular adder and modular subtractor are proposed to support high speed processing of LFSR operations. In addition, a family of special moduli are selected for high speed computation of modular operations. Though the area keeps the complexity of O(Nn^2) with no advantage in circuit level. The proposed architecture effectively reduces the time complexity from O(N log N) to linear time, O(N), compared to the best existing works. An FPGA implementation of the proposed architecture for LTV FHE is achieved and demonstrated. An elaborate comparison of the existing methods and the proposed work is presented, which shows the proposed work gains significant speed up over existing works

cuHE: A Homomorphic Encryption Accelerator Library

We introduce a CUDA GPU library to accelerate evaluations with homomorphic schemes defined over polynomial rings enabled with a number of optimizations including algebraic techniques for efficient evaluation, memory minimization techniques, memory and thread scheduling and low level CUDA hand-tuned assembly optimizations to take full advantage of the mass parallelism and high memory bandwidth GPUs offer. The arithmetic functions constructed to handle very large polynomial operands using number-theoretic transform (NTT) and Chinese remainder theorem (CRT) based methods are then extended to implement the primitives of the leveled homomorphic encryption scheme proposed by Löpez-Alt, Tromer and Vaikuntanathan. To compare the performance of the proposed CUDA library we implemented two applications: the Prince block cipher and homomorphic sorting algorithms on two GPU platforms in single GPU and multiple GPU configurations. We observed a speedup of 25 times and 51 times over the best previous GPU implementation for Prince with single and triple GPUs, respectively. Similarly for homomorphic sorting we obtained 12-41 times speedup depending on the number and size of the sorted elements

High Throughput Lattice-based Signatures on GPUs: Comparing Falcon and Mitaka

The US National Institute of Standards and Technology initiated a standardization process for post-quantum cryptography in 2017, with the aim of selecting key encapsulation mechanisms and signature schemes that can withstand the threat from emerging quantum computers. In 2022, Falcon was selected as one of the standard signature schemes, eventually attracting effort to optimize the implementation of Falcon on various hardware architectures for practical applications. Recently, Mitaka was proposed as an alternative to Falcon, allowing parallel execution of most of its operations. These recent advancements motivate us to develop high throughput implementations of Falcon and Mitaka signature schemes on Graphics Processing Units (GPUs), a massively parallel architecture widely available on cloud service platforms. In this paper, we propose the first parallel implementation of Falcon on various GPUs. An iterative version of the sampling process in Falcon, which is also the most time-consuming Falcon operation, was developed. This allows us to implement Falcon signature generation without relying on expensive recursive function calls on GPUs. In addition, we propose a parallel random samples generation approach to accelerate the performance of Mitaka on GPUs. We evaluate our implementation techniques on state-of-the-art GPU architectures (RTX 3080, A100, T4 and V100). Experimental results show that our Falcon-512 implementation achieves 58, 595 signatures/second and 2, 721, 562 verifications/second on an A100 GPU, which is 20.03× and 29.51× faster than the highly optimized AVX2 implementation on CPU. Our Mitaka implementation achieves 161, 985 signatures/second and 1, 421, 046 verifications/second on the same GPU. Due to the adoption of a parallelizable sampling process, Mitaka signature generation enjoys ≈ 2 – 20× higher throughput than Falcon on various GPUs. The high throughput signature generation and verification achieved by this work can be very useful in various emerging applications, including the Internet of Things

High-Speed Hardware Architectures and FPGA Benchmarking of CRYSTALS-Kyber, NTRU, and Saber

Performance in hardware has typically played a significant role in differentiating among leading candidates in cryptographic standardization efforts. Winners of two past NIST cryptographic contests (Rijndael in case of AES and Keccak in case of SHA-3) were ranked consistently among the two fastest candidates when implemented using FPGAs and ASICs. Hardware implementations of cryptographic operations may quite easily outperform software implementations for at least a subset of major performance metrics, such as latency, number of operations per second, power consumption, and energy usage, as well as in terms of security against physical attacks, including side-channel analysis. Using hardware also permits much higher flexibility in trading one subset of these properties for another. This paper presents high-speed hardware architectures for four lattice-based CCA-secure Key Encapsulation Mechanisms (KEMs), representing three NIST PQC finalists: CRYSTALS-Kyber, NTRU (with two distinct variants, NTRU-HPS and NTRU-HRSS), and Saber. We rank these candidates among each other and compare them with all other Round 3 KEMs based on the data from the previously reported work

TMVP-based Polynomial Convolution for Saber and Sable on GPU using CUDA-cores and Tensor-cores

Recently proposed lattice-based cryptography algorithms can be used to protect the IoT communication against the threat from quantum computers, but they are computationally heavy. In particular, polynomial multiplication is one of the most time-consuming operations in lattice-based cryptography. To achieve efficient implementation, the Number Theoretic Transform (NTT) algorithm is an ideal choice, but it has certain limitations on the parameters, which not all lattice-based schemes can employ directly. Hence, alternative techniques are proposed to accelerate polynomial multiplication on lattice-based schemes that cannot utilize the NTT directly. In this paper, we propose a parallel Toeplitz matrix-vector product (TMVP) version to accelerate the polynomial multiplication in PQC algorithms implemented it on a graphics processing unit (GPU). This is the first time a TMVP parallel version has been proposed and experimented on different GPU cores (i.e., CUDA-cores and Tensor-cores). The effectiveness of the proposed solution is validated on Saber (the NIST post-quantum standardization finalist) and Sable (an improved version of Saber) schemes. Experimental results show that TMVP-based polynomial convolution using CUDA-cores fails to exhibit a significant enhancement compared to the schoolbook CUDA-core method already proposed by Hafeez et al. 2023. However, when the TMVP technique is applied to Tensor-cores, it outperformed state-of-the-art implementations. The proposed Tensor-core approach outperformed the schoolbook Tensor-core method by up to 1.21×, and outperformed the dot-product-instructions method (Lee et al. 2022) by up to 3.63×. The proposed TMVP Tensor-cores is also faster than the TMVP CUDA-cores method by 13.76

HEAX: An Architecture for Computing on Encrypted Data

With the rapid increase in cloud computing, concerns surrounding data privacy, security, and confidentiality also have been increased significantly. Not only cloud providers are susceptible to internal and external hacks, but also in some scenarios, data owners cannot outsource the computation due to privacy laws such as GDPR, HIPAA, or CCPA. Fully Homomorphic Encryption (FHE) is a groundbreaking invention in cryptography that, unlike traditional cryptosystems, enables computation on encrypted data without ever decrypting it. However, the most critical obstacle in deploying FHE at large-scale is the enormous computation overhead. In this paper, we present HEAX, a novel hardware architecture for FHE that achieves unprecedented performance improvement. HEAX leverages multiple levels of parallelism, ranging from ciphertext-level to fine-grained modular arithmetic level. Our first contribution is a new highly-parallelizable architecture for number-theoretic transform (NTT) which can be of independent interest as NTT is frequently used in many lattice-based cryptography systems. Building on top of NTT engine, we design a novel architecture for computation on homomorphically encrypted data. We also introduce several techniques to enable an end-to-end, fully pipelined design as well as reducing on-chip memory consumption. Our implementation on reconfigurable hardware demonstrates 164-268x performance improvement for a wide range of FHE parameters.Comment: To appear in proceedings of ACM ASPLOS 202

CUDA-Accelerated RNS Multiplication in Word-Wise Homomorphic Encryption Schemes

Homomorphic encryption (HE), which allows computation over encrypted data, has often been used to preserve privacy. However, the computationally heavy nature and complexity of network topologies make the deployment of HE schemes in the Internet of Things (IoT) scenario difficult. In this work, we propose CARM, the first optimized GPU implementation that covers BGV, BFV and CKKS, targeting for accelerating homomorphic multiplication using GPU in heterogeneous IoT systems. We offer constant-time low-level arithmetic with minimum instructions and memory usage, as well as performance- and memory-prior configurations, and exploit a parametric and generic design, and offer various trade-offs between resource and efficiency, yielding a solution suitable for accelerating RNS homomorphic multiplication on both high-performance and embedded GPUs. Through this, we can offer more real-time evaluation results and relieve the computational pressure on cloud devices. We deploy our implementations on two GPUs and achieve up to 378.4×, 234.5×, and 287.2× speedup for homomorphic multiplication of BGV, BFV, and CKKS on Tesla V100S, and 8.8×, 9.2×, and 10.3× on Jetson AGX Xavier, respectively