We have located links that may give you full text access.
Logistic regression over encrypted data from fully homomorphic encryption.
BMC Medical Genomics 2018 October 12
BACKGROUND: One of the tasks in the 2017 iDASH secure genome analysis competition was to enable training of logistic regression models over encrypted genomic data. More precisely, given a list of approximately 1500 patient records, each with 18 binary features containing information on specific mutations, the idea was for the data holder to encrypt the records using homomorphic encryption, and send them to an untrusted cloud for storage. The cloud could then homomorphically apply a training algorithm on the encrypted data to obtain an encrypted logistic regression model, which can be sent to the data holder for decryption. In this way, the data holder could successfully outsource the training process without revealing either her sensitive data, or the trained model, to the cloud.
METHODS: Our solution to this problem has several novelties: we use a multi-bit plaintext space in fully homomorphic encryption together with fixed point number encoding; we combine bootstrapping in fully homomorphic encryption with a scaling operation in fixed point arithmetic; we use a minimax polynomial approximation to the sigmoid function and the 1-bit gradient descent method to reduce the plaintext growth in the training process.
RESULTS: Our algorithm for training over encrypted data takes 0.4-3.2 hours per iteration of gradient descent.
CONCLUSIONS: We demonstrate the feasibility but high computational cost of training over encrypted data. On the other hand, our method can guarantee the highest level of data privacy in critical applications.
METHODS: Our solution to this problem has several novelties: we use a multi-bit plaintext space in fully homomorphic encryption together with fixed point number encoding; we combine bootstrapping in fully homomorphic encryption with a scaling operation in fixed point arithmetic; we use a minimax polynomial approximation to the sigmoid function and the 1-bit gradient descent method to reduce the plaintext growth in the training process.
RESULTS: Our algorithm for training over encrypted data takes 0.4-3.2 hours per iteration of gradient descent.
CONCLUSIONS: We demonstrate the feasibility but high computational cost of training over encrypted data. On the other hand, our method can guarantee the highest level of data privacy in critical applications.
Full text links
Related Resources
Trending Papers
Consensus Statement on Vitamin D Status Assessment and Supplementation: Whys, Whens, and Hows.Endocrine Reviews 2024 April 28
The Tricuspid Valve: A Review of Pathology, Imaging, and Current Treatment Options: A Scientific Statement From the American Heart Association.Circulation 2024 April 26
Intravenous infusion of dexmedetomidine during the surgery to prevent postoperative delirium and postoperative cognitive dysfunction undergoing non-cardiac surgery: a meta-analysis of randomized controlled trials.European Journal of Medical Research 2024 April 19
Interstitial Lung Disease: A Review.JAMA 2024 April 23
Ventilator Waveforms May Give Clues to Expiratory Muscle Activity.American Journal of Respiratory and Critical Care Medicine 2024 April 25
Acute Kidney Injury and Electrolyte Imbalances Caused by Dapagliflozin Short-Term Use.Pharmaceuticals 2024 March 27
Systemic lupus erythematosus.Lancet 2024 April 18
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app