Please use this identifier to cite or link to this item:
https://scholarbank.nus.edu.sg/handle/10635/159892
DC Field | Value | |
---|---|---|
dc.title | PRIVACY THREATS AND PROTECTION ACROSS DATA LIFECYCLE | |
dc.contributor.author | YANG ZIQI | |
dc.date.accessioned | 2019-10-16T18:00:47Z | |
dc.date.available | 2019-10-16T18:00:47Z | |
dc.date.issued | 2019-01-24 | |
dc.identifier.citation | YANG ZIQI (2019-01-24). PRIVACY THREATS AND PROTECTION ACROSS DATA LIFECYCLE. ScholarBank@NUS Repository. | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/159892 | |
dc.description.abstract | As a massive amount of data is being aggregated into the cloud platforms powering our society, protecting user data privacy is one of the major challenges. User data faces different threats in different stages of data's lifecycle, which starts when user data is generated and continues when it is transmitted and eventually used. In this thesis, we investigate the threats to data privacy and develop protection mechanisms across the data lifecycle. Privacy threats in data lifecycle have two main types: explicit leakage in cloud platforms and implicit leakage in data processing. We develop isolation and inference techniques towards addressing both privacy threats. Specifically, this thesis makes three contributions. First, we propose an isolation framework in web systems to protect sensitive user data. Second, we apply machine learning algorithms to infer sensitive information from massive data. Finally, we study the privacy leakage in neural networks by proposing an effective inversion attack. | |
dc.language.iso | en | |
dc.subject | machine learning, neural networks, security, data privacy, natural language processing, web security | |
dc.type | Thesis | |
dc.contributor.department | COMPUTER SCIENCE | |
dc.contributor.supervisor | LIANG ZHENKAI | |
dc.description.degree | Ph.D | |
dc.description.degreeconferred | DOCTOR OF PHILOSOPHY | |
Appears in Collections: | Ph.D Theses (Open) |
Show simple item record
Files in This Item:
File | Description | Size | Format | Access Settings | Version | |
---|---|---|---|---|---|---|
YangZQ.pdf | 6.93 MB | Adobe PDF | OPEN | None | View/Download |
Google ScholarTM
Check
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.