8 research outputs found
Achieving GWAS with Homomorphic Encryption
One way of investigating how genes affect human traits would be with a
genome-wide association study (GWAS). Genetic markers, known as
single-nucleotide polymorphism (SNP), are used in GWAS. This raises privacy and
security concerns as these genetic markers can be used to identify individuals
uniquely. This problem is further exacerbated by a large number of SNPs needed,
which produce reliable results at a higher risk of compromising the privacy of
participants.
We describe a method using homomorphic encryption (HE) to perform GWAS in a
secure and private setting. This work is based on a proposed algorithm. Our
solution mainly involves homomorphically encrypted matrix operations and
suitable approximations that adapts the semi-parallel GWAS algorithm for HE. We
leverage the complex space of the CKKS encryption scheme to increase the number
of SNPs that can be packed within a ciphertext. We have also developed a cache
module that manages ciphertexts, reducing the memory footprint.
We have implemented our solution over two HE open source libraries, HEAAN and
SEAL. Our best implementation took minutes for a dataset with
samples, over covariates and SNPs.
We demonstrate that it is possible to achieve GWAS with homomorphic
encryption with suitable approximations
Privacy Preserving Computation in Home Loans using the FRESCO Framework
Secure Multiparty Computation (SMC) is a subfield of cryptography that allows multiple parties to compute jointly on a function without revealing their inputs to others. The technology is able to solve potential privacy issues that arises when a trusted third party is involved, like a server. This paper aims to evaluate implementations of Secure Multiparty Computation and its viability for practical use. The paper also seeks to understand and state the challenges and concepts of Secure Multiparty Computation through the construction of a home loan calculation application. Encryption over MPC is done within 2 to 2.5 Seconds. Up to 10K addition operations, MPC system performs very well and most applications will be sufficient within 10K additions
Towards the AlexNet Moment for Homomorphic Encryption: HCNN, theFirst Homomorphic CNN on Encrypted Data with GPUs
Deep Learning as a Service (DLaaS) stands as a promising solution for
cloud-based inference applications. In this setting, the cloud has a
pre-learned model whereas the user has samples on which she wants to run the
model. The biggest concern with DLaaS is user privacy if the input samples are
sensitive data. We provide here an efficient privacy-preserving system by
employing high-end technologies such as Fully Homomorphic Encryption (FHE),
Convolutional Neural Networks (CNNs) and Graphics Processing Units (GPUs). FHE,
with its widely-known feature of computing on encrypted data, empowers a wide
range of privacy-concerned applications. This comes at high cost as it requires
enormous computing power. In this paper, we show how to accelerate the
performance of running CNNs on encrypted data with GPUs. We evaluated two CNNs
to classify homomorphically the MNIST and CIFAR-10 datasets. Our solution
achieved a sufficient security level (> 80 bit) and reasonable classification
accuracy (99%) and (77.55%) for MNIST and CIFAR-10, respectively. In terms of
latency, we could classify an image in 5.16 seconds and 304.43 seconds for
MNIST and CIFAR-10, respectively. Our system can also classify a batch of
images (> 8,000) without extra overhead
High-Performance FV Somewhat Homomorphic Encryption on GPUs: An Implementation using CUDA
Homomorphic encryption (HE) offers great capabilities that can solve a wide range of privacy-preserving computing problems. This tool allows anyone to process encrypted data producing encrypted results that only the decryption key’s owner can decrypt. Although HE has been realized in several public implementations, its performance is quite demanding. The reason for this is attributed to the huge amount of computation required by secure HE schemes. In this work, we present a CUDAbased implementation of the Fan and Vercauteren (FV) Somewhat HomomorphicEncryption (SHE) scheme. We demonstrate several algebraic tools such as the Chinese Remainder Theorem (CRT), Residual Number System (RNS) and Discrete Galois Transform (DGT) to accelerate and facilitate FV computation on GPUs. We also show how the entire FV computation can be done on GPU without multi-precision arithmetic. We compare our GPU implementation with two mature state-of-the-art implementations: 1) Microsoft SEAL v2.3.0-4 and 2) NFLlib-FV. Our implementation outperforms them and achieves on average 5.37x, 7.37x, 22.22x, 5.11x and 13.18x (resp. 2.03x, 2.94x, 27.86x, 8.53x and 18.69x) for key generation, encryption, decryption, homomorphic addition and homomorphic multiplication against SEAL-FVRNS (resp. NFLlib-FV)
CoVnita, an end-to-end privacy-preserving framework for SARS-CoV-2 classification
Abstract Classification of viral strains is essential in monitoring and managing the COVID-19 pandemic, but patient privacy and data security concerns often limit the extent of the open sharing of full viral genome sequencing data. We propose a framework called CoVnita, that supports private training of a classification model and secure inference with the same model. Using genomic sequences from eight common SARS-CoV-2 strains, we simulated scenarios where the data was distributed across multiple data providers. Our framework produces a private federated model, over 8 parties, with a classification AUROC of 0.99, given a privacy budget of ε = 1 . The roundtrip time, from encryption to decryption, took a total of 0.298 s, with an amortized time of 74.5 ms per sample