Privacy Issues in Lifelogging and Self Quantification Blaine
Privacy Issues in Lifelogging and Self Quantification Blaine Price
Background • Open University School of Computing Researchers Examining Privacy in Ubi. Comp for 6 years across EPSRC and ERC funded projects: – PRi. MMA: Privacy Rights Management in Mobile Apps – Adaptive Security and Privacy – Privacy Dynamics – Monetize Me
Lifelogging & Self-Quantification • Lifelogging popularized by Gemmel & Bell • Self-Quantification ‘Movement’ popularlized by Wolf • Everyone is a self-quantifier (at some time)
PRi. MMA • Privacy Rights Management for Mobile Apps • Studied location tracking apps using within families • Developed a research method for studying privacy implications for new technologies – Contravision
Adaptive Security and Privacy • 5 year ERC project covering broad security & privacy issues in ubiquitous computing • Privacy Requirements for Smart Home & Self. Quantification technologies • Energy data obfuscation to prevent privacy attacks
Privacy Dynamics • 3 year EPSRC project: group privacy in social & ubiquitous computing (ML/HCI/Social Psych) • Analyzing social identity to prevent social media sharing across incompatible groups • Analysing photo sharing behaviour with groups wearing lifelogging cameras
Privacy Dynamics cont’d • Patients recovering from knee surgery, pain and activity logging and sharing • Young breast cancer survivors, measuring depression indicators, co-location, leaving home, sleeping, activity, social media
Monetize Me • New Business models for privacy and selfquantification in the digital economy • Contravision studies: – Health tracking – Financial tracking
Demo Media
Monetize Me cont’d • Understanding privacy requirements for selfquantification in different domains • Building a research infrastructure supporting privacy sensitive business models
Upcoming Workshop • Autumn workshop (date to be announced) • Find out more about studies and open data collection infrastructure • Contact Blaine. Price@open. ac. uk or see www. monetizeme. co. uk
An example: passive sleep data
A normal night
- Slides: 15