15849

SecureMed: Secure Medical Computation using GPU-Accelerated Homomorphic Encryption Scheme

Alhassan Khedr, Glenn Gulak
Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada
Cryptology ePrint Archive: Report 2016/445, 2016
@article{scheme2016securemed,

   title={SecureMed: Secure Medical Computation using GPU-Accelerated Homomorphic Encryption Scheme},

   author={Scheme, GPU-Accelerated Homomorphic Encryption},

   year={2016}

}

Download Download (PDF)   View View   Source Source   

380

views

Sharing the medical records of individuals among healthcare providers and researchers around the world can accelerate advances in medical research. While the idea seems increasingly practical due to cloud data services, maintaining patient privacy is of paramount importance. Standard encryption algorithms help protect sensitive data from outside attackers but they cannot be used to compute on this sensitive data while being encrypted. Homomorphic Encryption (HE) presents a very useful tool that can compute on encrypted data without the need to decrypt it. In this work, we describe an optimized NTRUbased implementation of the GSW homomorphic encryption scheme. Our results show a factor of 58x improvement in CPU performance compared to other recent work on encrypted medical data under the same security settings. Our system is built to be easily portable to GPUs resulting in an additional speedup of up to a factor of 104x (and 410x) to offer an overall speedup of 6085x (and 24011x) using a single GPU (or four GPUs), respectively.
VN:F [1.9.22_1171]
Rating: 5.0/5 (3 votes cast)
SecureMed: Secure Medical Computation using GPU-Accelerated Homomorphic Encryption Scheme, 5.0 out of 5 based on 3 ratings

* * *

* * *

TwitterAPIExchange Object
(
    [oauth_access_token:TwitterAPIExchange:private] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
    [oauth_access_token_secret:TwitterAPIExchange:private] => o29ji3VLVmB6jASMqY8G7QZDCrdFmoTvCDNNUlb7s
    [consumer_key:TwitterAPIExchange:private] => TdQb63pho0ak9VevwMWpEgXAE
    [consumer_secret:TwitterAPIExchange:private] => Uq4rWz7nUnH1y6ab6uQ9xMk0KLcDrmckneEMdlq6G5E0jlQCFx
    [postfields:TwitterAPIExchange:private] => 
    [getfield:TwitterAPIExchange:private] => ?cursor=-1&screen_name=hgpu&skip_status=true&include_user_entities=false
    [oauth:protected] => Array
        (
            [oauth_consumer_key] => TdQb63pho0ak9VevwMWpEgXAE
            [oauth_nonce] => 1480740767
            [oauth_signature_method] => HMAC-SHA1
            [oauth_token] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
            [oauth_timestamp] => 1480740767
            [oauth_version] => 1.0
            [cursor] => -1
            [screen_name] => hgpu
            [skip_status] => true
            [include_user_entities] => false
            [oauth_signature] => F1ELF8JcKz3N8wmzdzBsxDyt4KU=
        )

    [url] => https://api.twitter.com/1.1/users/show.json
)
Follow us on Facebook
Follow us on Twitter

HGPU group

2080 peoples are following HGPU @twitter

HGPU group © 2010-2016 hgpu.org

All rights belong to the respective authors

Contact us: