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@article{faucris.204340090,
abstract = {An individual's genetic information is possibly the most valuable
personal information. While knowledge of a person's DNA sequence can
facilitate the diagnosis of several heritable diseases and allow
personalized treatment, its exposure comes with significant threats to
the patient's privacy. Currently known solutions for privacy-respecting
computation require the owner of the DNA to either be heavily involved
in the execution of a cryptographic protocol or to completely outsource
the access control to a third party. This motivates the demand for
cryptographic protocols which enable computation over encrypted genomic
data while keeping the owner of the genome in full control. We envision a
scenario where data owners can exercise arbitrary and dynamic access
policies, depending on the intended use of the analysis results and on
the credentials of who is conducting the analysis. At the same time,
they are not required to maintain a local copy of their entire genetic
data and do not need to exhaust their computational resources in an
expensive cryptographic protocol.
In this work, we present METIS, a system that assists the computation over encrypted data stored in the cloud while leaving the decision on admissible computations to the data owner. A critical feature of our system is that the data owner is free from computational overload and her communication complexity is independent of the size of the input data and only linear in the size of the circuit's output. METIS is based on garbled circuits and supports any polynomially-computable function. We demonstrate the practicality of our approach with an implementation and an evaluation of several functions over real dataset}, author = {Deuber, Dominic and Egger, Christoph and Fech, Katharina and Malavolta, Giulio and Schröder, Dominique and Thyagarajan, Sri Aravinda Krishnan and Battke, Florian and Durand, Claudia}, doi = {10.2478/popets-2019-0007}, faupublication = {yes}, journal = {Proceedings on Privacy Enhancing Technologies}, pages = {108-132}, peerreviewed = {Yes}, title = {{My} {Genome} {Belongs} to {Me}: {Controlling} {Third} {Party} {Computation} on {Genomic} {Data}}, volume = {2019}, year = {2019} }