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Garbled Circuit (GC) is a basic technique for practical secure computation. GC handles Boolean circuits; it consumes significant network bandwidth to transmit encoded gate truth tables, each of which scales with the computational security parameter κ. GC optimizations that reduce bandwidth consumption are valuable. It is natural to consider a generalization of Boolean two-input one-output gates (represented by 4-row one-column lookup tables, LUTs) to arbitrary N-row m-column LUTs. Known techniques for this do not scale, with na¨ıve size-O(Nmκ) garbled LUT being the most practical approach in many scenarios. Our novel garbling scheme – logrow – implements GC LUTs while sending only a logarithmic in N number of ciphertexts! Specifically, let n = ⌈log2 N⌉. We allow the GC parties to evaluate a LUT for (n−1)κ+nmκ+Nm bits of communication. logrow is compatible with modern GC advances, e.g. half gates and free XOR. Our work improves state-of-the-art GC handling of several interesting applications, such as privacypreserving machine learning, floating-point arithmetic, and DFA evaluation. 

MagmaDNN [17] is a deep learning framework driven using the highly optimized MAGMA dense linear algebra package. The library offers comparable performance to other popular frameworks, such as TensorFlow, PyTorch, and Theano. C++ is used to implement the framework providing fast memory operations, direct cuda access, and compile time errors. Common neural network layers such as Fully Connected, Convolutional, Pooling, Flatten, and Dropout are included. Hyperparameter tuning is performed with a parallel grid search engine. MagmaDNN uses several techniques to accelerate network training. For instance, convolutions are performed using the Winograd algorithm and FFTs. Other techniques include MagmaDNNs custom memory manager, which is used to reduce expensive memory transfers, and accelerated training by distributing batches across GPU nodes. This paper provides an overview of the MagmaDNN framework and how it leverages the MAGMA library to attain speed increases. This paper also addresses how deep networks are accelerated by training in parallel and further challenges with parallelization.