Controlled sharing of body-sensor data for sports analytics using code consent capabilities
Permanent link
https://hdl.handle.net/10037/6502View/ Open
Source code of the implementation (Unknown)
(PDF)
Date
2014-05-15Type
Master thesisMastergradsoppgave
Author
Zhang, WeiAbstract
With the advent of body sensor technology, athletes can easily record individual
physiological metrics such as heart rate, steps, and blood sugar. In parallel,
there is an increasing number of web services that use the raw body-sensor
data as input to sports analytics. For the individual athletes, this can yield
valuable insights on their performance and suggestions on individual training
programs, which consequently aid their development.
Once the data is imported into these analytics systems, the athletes are however
left with little control over their data. This thesis presents code consent,
a user-centric mechanism which combines informed consent and capabilities
to enables athletes to share their private data in a more controllable manner.
Furthermore, it gives both the athletes and analytical services the extensibility,
flexibility to delegate the authority across protect domains by chaining keyed
cryptographic hashes.
The action and terms of informed consent are transformed to the reference
to the source code and attributes of a capability. When executing a capability,
the policy of access control to the resource is enforced, and the operation to
the resource is performed in OpenCPU server which is a R sandbox. With a
use case, we demonstrate now a user is able to share with others a graph of his
aggregated data by delegating a capability. This paper details the implementation
of constructing a code consent capability, and verification, delegation,
execution of a capability. The security of the prototype is also discussed when
users revokes capabilities. In the prototype implementation, we also evaluate
the end-to-end latency of executing a capability, which includes the time of
verifying the signature, the time of executing the program code, as well as
downloading the output file. The analysis of the performance guides us to
investigate the optimization of our prototype such as capability cache and
function chaining.
Publisher
UiT Norges arktiske universitetUiT The Arctic University of Norway
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