Efficient Privacy-Preserving Big Data Processing  through Proxy-Assisted ORAM

Nikolaos P.  Karvelas; Andreas Peter; Stefan Katzenbeisser; Sebastian Biedermann

Paper 2014/072

Efficient Privacy-Preserving Big Data Processing through Proxy-Assisted ORAM

Nikolaos P. Karvelas, Andreas Peter, Stefan Katzenbeisser, and Sebastian Biedermann

Abstract

We present a novel mechanism that allows a client to securely outsource his private data to the cloud while at the same time to delegate to a third party the right to run certain algorithms on his data. The mechanism is privacy-preserving, meaning that the third party only learns the result of his algorithm on the client's data, while at the same time the access pattern on the client's data is hidden from the cloud. To achieve this we combine recent advances in the field of Oblivious RAM and Secure Two-Party Computation: We develop an Oblivious RAM which is ran between the cloud and a proxy server, and which does not need the data to be decrypted at any point. The evaluation on the data is done by employing Yao's garbled circuit solution for Secure Two-Party Computation.

Metadata

Available format(s): PDF
Publication info: Preprint. MINOR revision.
Contact author(s): karvelas @ seceng informatik tu-darmstadt de
History: 2014-02-04: received
Short URL: https://ia.cr/2014/072
License: CC BY

BibTeX

@misc{cryptoeprint:2014/072,
      author = {Nikolaos P.  Karvelas and Andreas Peter and Stefan Katzenbeisser and Sebastian Biedermann},
      title = {Efficient Privacy-Preserving Big Data Processing  through Proxy-Assisted {ORAM}},
      howpublished = {Cryptology {ePrint} Archive, Paper 2014/072},
      year = {2014},
      url = {https://eprint.iacr.org/2014/072}
}