Chris Elsmore
Anil Madhavapeddy
ABSTRACT
We discuss the problem of building a user-acceptable infrastructure for a large organisation that wishes to measure its employees' travel-to-work carbon footprint, based on the gathering of high resolution geolocation data on employees in a privacy-sensitive manner. This motivated the construction of a distributed system of personal containers in which individuals record fine-grained location information into a private data-store which they own, and from which they can trade portions of data to the organisation in return for specific benefits. This framework can be extended to gather a wide variety of personal data and facilitates the transformation of private information into a public good, with minimal and assessable loss of individual privacy.
This is currently a work in progress. We report on the hardware, software and social aspects of piloting this scheme on the University of Cambridge's experimental cloud service, as well as contrasting it to a traditional centralised model.
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Index Terms
- Confidential carbon commuting: exploring a privacy-sensitive architecture for incentivising 'greener' commuting
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