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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...
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...
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...
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...
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...
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...
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 ...
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...
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 ...
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...
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...
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 ...
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...
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...
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...

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