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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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Department:
University of Oxford
Role:
Author

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Role:
Supervisor
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Supervisor
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Grant:
EP/F500394/1
Funding agency for:
Pedro M. Esperança
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford
Language:
English

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