Thesis
Privacy-preserving statistical and machine learning methods under fully homomorphic encryption
- Abstract:
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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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Funding
Bibliographic Details
- Type of award:
- DPhil
- Level of award:
- Doctoral
- Awarding institution:
- University of Oxford
Item Description
- Language:
- English
- UUID:
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uuid:a081311c-b25c-462e-a66b-1e4ac4de5fc2
- Deposit date:
- 2017-01-17
Terms of use
- Copyright holder:
- Esperança, P
- Copyright date:
- 2016
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