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nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data
Privacy-Preserving Machine Learning Deep Learning Graph Compilers
2019/8/21
In previous work, Boemer et al. introduced nGraph-HE, an extension to the Intel nGraph deep learning (DL) compiler, that en- ables data scientists to deploy models with popular frameworks such as Tens...
Towards a Practical Clustering Analysis over Encrypted Data
clustering mean-shift homomorphic encryption, privacy
2019/5/13
Clustering analysis is one of the most significant unsupervised machine learning tasks, and it is utilized in various fields associated with privacy issue including bioinformatics, finance and image p...
Efficient coding for secure computing with additively-homomorphic encrypted data
packing batching homomorphic encryption
2019/5/5
A framework is introduced for efficiently computing with encrypted data. We assume a semi-honest security model with two computing parties. Two different coding techniques are used with additively hom...
Oblivious PRF on Committed Vector Inputs and Application to Deduplication of Encrypted Data
public-key cryptography applications pseudo-random functions
2019/5/5
Ensuring secure deduplication of encrypted data is a very active topic of research because deduplication is effective at reducing storage costs. Schemes supporting deduplication of encrypted data that...
nGraph-HE: A Graph Compiler for Deep Learning on Homomorphically Encrypted Data
Homomorphic encryption intermediate representation deep learning
2019/4/3
Homomorphic encryption (HE)---the ability to perform computation on encrypted data---is an attractive remedy to increasing concerns about data privacy in deep learning (DL). However, building DL model...
Semi-parallel Logistic Regression for GWAS on Encrypted Data
Homomorphic encryption Genome-wide association studies Logistic regression
2019/3/21
The sharing of biomedical data is crucial to enable scientific discoveries across institutions and improve health care. For example, genome-wide association studies (GWAS) based on a large number of s...
FPGA-based High-Performance Parallel Architecture for Homomorphic Computing on Encrypted Data
FV homomorphic encryption latticebased cryptography polynomial multiplication
2019/2/25
Homomorphic encryption is a tool that enables computation on encrypted data and thus has applications in privacy-preserving cloud computing. Though conceptually amazing, implementation of homomorphic ...
Setup-Free Secure Search on Encrypted Data: Faster and Post-Processing Free
Secure search Fully homomorphic encryption Randomized algorithms
2019/1/2
We present a novel secure searchsecure search protocol on data and queries encrypted with Fully Homomorphic Encryption (FHE). Our protocol enables organizations (client) to (1) securely upload an unso...
Ciphertext-Policy Attribute-Based Encrypted Data Equality Test and Classification
Attribute-Based Encryption Authorization Classification
2018/11/5
Thanks to the ease of access and low expenses, it is now popular for people to store data in cloud servers. To protect sensitive data from being leaked to the outside, people usually encrypt the data ...
Decentralized Evaluation of Quadratic Polynomials on Encrypted Data
Decentralization FHE 2-DNF
2018/11/2
Machine learning and group testing are quite useful methods for many different fields such as finance, banks, biology, medicine, etc. These application domains use quite sensitive data, and huge amoun...
The AlexNet Moment for Homomorphic Encryption: HCNN, the First Homomorphic CNN on Encrypted Data with GPUs
Fully Homomorphic Encryption Deep Learning Encrypted CNN
2018/11/2
Fully homomorphic encryption, with its widely-known feature of computing on encrypted data, empowers a wide range of privacy-concerned cloud applications including deep learning as a service. This com...
Efficient Logistic Regression on Large Encrypted Data
implementation machine learning homomorphic encryption
2018/7/10
Machine learning on encrypted data is a cryptographic method for analyzing private and/or sensitive data while keeping privacy. In the training phase, it takes as input an encrypted training data and ...
Logistic regression over encrypted data from fully homomorphic encryption
homomorphic encryption logistic regression
2018/5/22
More precisely, given a list of approximately 15001500 patient records, each with 1818 binary features containing information on specific mutations, the idea was for the data holder to encrypt the rec...
Unsupervised Machine Learning on Encrypted Data
Machine Learning Clustering Fully Homomorphic Encryption
2018/5/11
In the context of Fully Homomorphic Encryption, which allows computations on encrypted data, Machine Learning has been one of the most popular applications in the recent past. All of these works, howe...
Frequency-smoothing encryption: preventing snapshot attacks on deterministically-encrypted data
encrypted database snapshot attack inference attack
2017/11/13
Naveed, Kamara, and Wright (CCS 2015) applied classical frequency analysis to carry out devastating inference attacks on databases in which the columns are encrypted with deterministic and order-prese...