Research > Data Privacy and Monetization
Data Privacy and Monetization Research at Technicolor Palo Alto
Data Privacy and Monetization Projects
Mobile Systems Privacy: We are building mechanisms that enable users to monitor and control the use of personal data by applications on mobile devices.
Privacy and Monetization: Our privacy research focuses on technology for the digital home and the interaction of the home and cloud networks. People living in digital homes will interact with numerous recommendation services to get advice not just on which movies to watch, and which restaurants to try, but also to receive health advice, suggestions for home energy management, and so on. Each of these recommendation services requires different profile data about a person, however there is also a fair amount of overlap in the profile data used across these services. Managing your personal data and its interaction with these various services in a way that allows users the privacy they seek, is one of our key goals. We are designing and experimenting with privacy preserving recommendation services that can still provide good recommendations yet only use vague summaries of your data and thus cannot distinguish you from thousands of other people. In addition, we focus on how to design architectures that enable recommendation services to be implemented partially in the cloud and partially in the home, and aim to quantify the resulting level of privacy offered for each architecture.
People
- Jean Bolot |
- Smriti Bhagat |
- Aleksandar Nikolov |
- Stratis Ioannidis |
- Nadia Fawaz |
- Nina Taft |
- Anmol Sheth |
- Pranav Dandekar |
- Udi Weinsberg
Publications
- TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones, OSDI, 2010 [PDF]
- Sensor Tricorder: What does that sensor know about me?, HotMobile, 2011 [PDF]
- Mining and Modeling Large Scale Cell Phone Data, ACM SIGACT/SIGMOBILE International Workshop on Foundations of Mobile Computing, FOMC, 2011
- Anonymization of location data does not work: a large-scale measurement study, ACM Mobicom, 2011
- Learning in a Large Function Space: Privacy-Preserving mechanisms for SVM Learning, Journal of Privacy and Confidentiality, 2012
- Private linear maps under decaying privacy, Information Theory and Applications Workshop (ITA), 2012