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Microsoft Research co-develops cloud data scrambler

'Melbourne Shuffle' will make it harder for cloud operators to mine or sniff your data

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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 sufficient to achieve privacy protection, because the data access patterns that users exhibit can reveal information about the content of their data”.

The paper goes on to explain that one solution to this issue is “data-oblivious algorithms and storage, which hide data access patterns … by obfuscating a sequence of data accesses intended by a client by simulating it with the one that appears indistinguishable from a random sequence of data accesses.”

Such algorithms work well, but are labelled “computationally expensive”.

The paper proposes an alternative: a “Melbourne shuffle” using techniques derived from card shuffling to disguise the true nature of data being accessed in the cloud.

You'll need your scientific calculator and rather advanced mathematical knowledge to make sense of the next few of the paper's pages. The long and short of it is that the authors feel they've cracked a way to anonymise your data without slowing down cloud storage systems, partly by using decoys and partly by obfuscating the data most likely to be subject to analysis by a snooper. Importantly, they also feel that the Melbourne Shuffle won't expand the amount of storage capacity required, therefore keeping bills for elastic storage under control, or requiring extra work by servers that would also incur extra charges.

If valid, this work will doubtless be welcomed by many users in these post-Snowden times. Perhaps those users will include Microsoft, which has like many technology companies expressed its distaste with the extent of State surveillance revealed by Russia's most famous guest. ®

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