The University of Chicago Hermetic: Privacy-preserving Distributed Analytics without (most) Side Channels a Thesis Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Science Department of Computer Science by Min Xu

@inproceedings{2017TheUO,
  title={The University of Chicago Hermetic: Privacy-preserving Distributed Analytics without (most) Side Channels a Thesis Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Science Department of Computer Science by Min Xu},
  author={},
  year={2017}
}
  • Published 2017
Distributed analytics systems, such as Spark, enable users to efficiently perform computations over large distributed data sets. Recently, a number of systems have been proposed that can additionally protect the privacy of the data, by keeping it encrypted even in memory, and by performing the computations using trusted hardware features, such as Intel’s… CONTINUE READING