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Thesis

Privacy-preserving statistical and machine learning methods under fully homomorphic encryption

Abstract:

Advances in technology have now made it possible to monitor heart rate, body temperature and sleep patterns; continuously track movement; record brain activity; and sequence DNA in the jungle --- all using devices that fit in the palm of a hand. These and other recent developments have sparked interest in privacy-preserving methods: computational approaches which are able to utilise the data without leaking subjects' personal information.

Classical encryption techniques have been us...

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Division:
MPLS
Department:
Doctoral Training Centre - MPLS
Department:
University of Oxford
Role:
Author

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Role:
Supervisor
Role:
Supervisor
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Funding agency for:
Esperança, P
Grant:
EP/F500394/1
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford
Language:
English
UUID:
uuid:a081311c-b25c-462e-a66b-1e4ac4de5fc2
Deposit date:
2017-01-17

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