Microsoft Research co-develops cloud data scrambler

Researchers from Microsoft, the University of California, Irvine and Brown University have proposed a technology that should make it harder to derive value from data stored in the cloud.

In a paper titled The Melbourne Shuffle: Improving Oblivious Storage in the Cloud, authors Olga Ohrimenko, Michael T. Goodrich, Roberto Tamassia and Eli Upfal kick things off with the statement that “One of the unmistakable recent trends in networked computation and distributed information management is that of cloud storage, whereby users outsource data to external servers that manage and provide access to their data.”

“Such services also introduce privacy concerns,” the quartet write, because “, it is likely that cloud storage providers will want to perform data mining on user data, and it is also possible that such data will be subject to government searches. Thus, there is a need for algorithmic solutions that preserve the desirable properties of cloud storage while also providing privacy protection for user data.”

Encryption alone, they continue, “is not suf?cient to achieve privacy protection, because the data access patterns that users exhibit can reveal information about the content of their data”.
 
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