YouProve:Authenticity and Fidelity in Mobile Sensing

Peter Gilbert, Jaeyeon Jung, Kyungmin Lee, Henry Qin, Daniel Sharkey, Anmol Sheth, and Landon P. Cox


As more services have come to rely on sensor data such

as audio and photos collected by mobile phone users, verifying

the authenticity of this data has become critical for service

correctness. At the same time, clients require the flexibility

to tradeoff the fidelity of the data they contribute for resource

efficiency or privacy. This paper describes YouProve,

a partnership between a mobile device’s trusted hardware

and software that allows untrusted client applications to directly

control the fidelity of data they upload and services

to verify that the meaning of source data is preserved. The

key to our approach is trusted analysis of derived data, which

generates statements comparing the content of a derived data

item to its source. Experiments with a prototype implementation

for Android demonstrate that YouProve is feasible.

Our photo analyzer is over 99% accurate at identifying regions

changed only through meaning-preserving modifications

such as cropping, compression, and scaling. Our audio

analyzer is similarly accurate at detecting which sub-clips of

a source audio clip are present in a derived version, even in

the face of compression, normalization, splicing, and other

modifications. Finally, performance and power costs are reasonable,

with analyzers having little noticeable effect on interactive

applications and CPU-intensive analysis completing

asynchronously in under 70 seconds for 5-minute audio

clips and under 30 seconds for 5-megapixel photos.


Publication typeInproceedings
PublisherACM SenSys
> Publications > YouProve:Authenticity and Fidelity in Mobile Sensing