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Exploratory Data Analysis (EDA) is a crucial step in any data science project. However, existing Python libraries fall short in supporting data scientists to complete common EDA tasks for statistical modeling. Their API design is either too low level, which is optimized for plotting rather than EDA, or too high level, which is hard to specify more fine-grained EDA tasks. In response, we propose DataPrep.EDA, a novel task-centric EDA system in Python. DataPrep.EDA allows data scientists to declaratively specify a wide range of EDA tasks in different granularity with a single function call. We identify a number of challenges to implement DataPrep.EDA, and propose effective solutions to improve the scalability, usability, customizability of the system. In particular, we discuss some lessons learned from using Dask to build the data processing pipelines for EDA tasks and describe our approaches to accelerate the pipelines. We conduct extensive experiments to compare DataPrep.EDA with Pandas-profiling, the state-of-the-art EDA system in Python. The experiments show that DataPrep.EDA significantly outperforms Pandas-profiling in terms of both speed and user experience. DataPrep.EDA is open-sourced as an EDA component of DataPrep: https://github.com/sfu-db/dataprep. 

Hybrid Privacy-Preserving Neural Network (HPPNN) implementing linear layers by Homomorphic Encryption (HE) and nonlinear layers by Garbled Circuit (GC) is one of the most promising secure solutions to emerging Machine Learning as a Service (MLaaS). Unfortunately, a HPPNN suffers from long inference latency, e.g., ∼100 seconds per image, which makes MLaaS unsatisfactory. Because HE-based linear layers of a HPPNN cost 93% inference latency, it is critical to select a set of HE parameters to minimize computational overhead of linear layers. Prior HPPNNs over-pessimistically select huge HE parameters to maintain large noise budgets, since they use the same set of HE parameters for an entire network and ignore the error tolerance capability of a network. In this paper, for fast and accurate secure neural network inference, we propose an automated layer-wise parameter selector, AutoPrivacy, that leverages deep reinforcement learning to automatically determine a set of HE parameters for each linear layer in a HPPNN. The learning-based HE parameter selection policy outperforms conventional rule-based HE parameter selection policy. Compared to prior HPPNNs, AutoPrivacy-optimized HPPNNs reduce inference latency by 53%∼70% with negligible loss of accuracy.