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If anyone had any doubt that private machine learning is a growing area then this week might take care of that.

Papers

Secure multiparty computation:

Homomorphic encryption:

  • Unsupervised Machine Learning on Encrypted Data
    Implements K-means privately using fully homomorphic encryption and a bit-wise rational encoding, with suggestions for tweaking K-means to make it more practical for this setting. The TFHE library (see next) is used for experiments.
  • TFHE: Fast Fully Homomorphic Encryption over the Torus
    Proclaimed as the fastest FHE library currently available, this paper is the extended version of previous descriptions of the underlying scheme and optimizations.
  • Homomorphic Secret Sharing: Optimizations and Applications
    Further work on a hybrid scheme between homomorphic encryption and secret sharing: operations can be performed locally by each share holder as in the former, yet a final combination is needed in the end to recover the result as in the latter: "this enables a level of compactness and efficiency of reconstruction that is impossible to achieve via standard FHE".

Secure enclaves:

Differential privacy:

Bonus

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