The research will enable the auditing and control of personally identifiable information leaks, addressing the key challenges of how to identify and control PII leaks when users’ PII is not known a priori, nor is the set of apps or devices that leak this information. First, to enable auditing through improved transparency, we are investigating how to use machine learning to reliably identify PII from network flows, and identify algorithms that incorporate user feedback to adapt to the changing landscape of privacy leaks. Second, we are building tools that allow users to control how their information is (or not) shared with other parties. Third, we are investigating the extent to which our approach extends to privacy leaks from IoT devices. Besides adapting our system to the unique format for leaks across a variety of IoT devices, our work investigates PII exposed indirectly through time-series data produced by IoT-generated monitoring.
The research will enable the auditing and control of personally identifiable information leaks, addressing the key challenges of how to identify and control PII leaks when users’ PII is not known a priori, nor is the set of apps or devices that leak this information. First, to enable auditing through improved transparency, we are investigating how to use machine learning to reliably identify PII from network flows, and identify algorithms that incorporate user feedback to adapt to the changing landscape of privacy leaks. Second, we are building tools that allow users to control how their information is (or not) shared with other parties. Third, we are investigating the extent to which our approach extends to privacy leaks from IoT devices. Besides adapting our system to the unique format for leaks across a variety of IoT devices, our work investigates PII exposed indirectly through time-series data produced by IoT-generated monitoring.
