PRIVACY THREATS AND PROTECTION ACROSS DATA LIFECYCLE

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.