If anyone had any doubt that private machine learning is a growing area then this week might take care of that.
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.
- 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".
- 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.
- 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.
- An Empirical Analysis of Anonymity in Zcash
A little reminder that anonymity is hard.