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:
- ABY3: A Mixed Protocol Framework for Machine Learning
One of big guys in secure computation for ML is back with new protocols in the 3-server setting for training linear regression, logistic regression, and neural network models. Impressive performance improvements for both training and prediction. - EPIC: Efficient Private Image Classification (or: Learning from the Masters)
An update to work from last year on efficient private image classification using SPDZ and support vector machines. Includes great overview of recent related work.
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:
- SecureCloud: Secure Big Data Processing in Untrusted Clouds
An joint European research project to develop a platform for pushing critical applications to untrusted cloud environments, using secure enclaves and supporting big data. Envisioned use cases from finance, health care, and smart grids. - SecureStreams: A Reactive Middleware Framework for Secure Data Stream Processing
Presents concrete work done in the above SecureCloud project, namely a high-level Lua-based framework for privately processing streams at scale using dataflow programming and secure enclaves.
Differential privacy:
- Privately Learning High-Dimensional Distributions
Tackles the problem that privacy “comes almost for free when data is low-dimensional but comes at a steep price when data is high-dimensional” as measured in amount of samples needed. Two mechanisms are presented for learning respectively a multivariate Gaussian and a product distribution. - SynTF: Synthetic and Differentially Private Term Frequency Vectors for Privacy-Preserving Text Mining
A differentially private mechanism is used to prevent author re-identification in texts used for training models where anomymized feature vectors can be used instead of the actual body text. Concrete experiments include topic classification of newsgroups postings. - Distributed Differentially-Private Algorithms for Matrix and Tensor Factorization
Correlated noise is used to privately perform two common operations via a centralized but curious party or directly between data holders, respectively. Interestingly, the correlated noise is not uniform as in typical secure aggregation settings.
Bonus
- An Empirical Analysis of Anonymity in Zcash
A little reminder that anonymity is hard.
