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Research is increasingly showing the tremendous vulnerability in machine learning models to seemingly undetectable adversarial inputs. One of the current limitations in adversarial machine learning research is the incredibly time-consuming testing of novel defenses against various attacks and across multiple datasets, even with high computing power. To address this limitation, we have developed Jespipe as a new plugin-based, parallel-by-design Open MPI framework that aids in evaluating the robustness of machine learning models. The plugin-based nature of this framework enables researchers to specify any pre-training data manipulations, machine learning models, adversarial models, and analysis or visualization metrics with their input Python files. Because this framework is plugin-based, a researcher can easily incorporate model implementations using popular deep learning libraries such as PyTorch, Keras, TensorFlow, Theano, or MXNet, or adversarial robustness tools such as IBM’s Adversarial Robustness Toolbox or Foolbox. The parallelized nature of this framework also enables researchers to evaluate various learning or attack models with multiple datasets simultaneously by specifying all the models and datasets they would like to test with our XML control file template. Overall, Jespipe shows promising results by reducing latency in adversarial machine learning algorithm development and testing compared to traditional Jupyter notebook workflows. 

Recently, blockchain has received much attention from the mobility-centric Internet of Things (IoT). It is deemed the key to ensuring the built-in integrity of information and security of immutability by design in the peer-to-peer network (P2P) of mobile devices. In a permissioned blockchain, the authority of the system has control over the identities of its users. Such information can allow an ill-intentioned authority to map identities with their spatiotemporal data, which undermines the location privacy of a mobile user. In this paper, we study the location privacy preservation problem in the context of permissioned blockchain-based IoT systems under three conditions. First, the authority of the blockchain holds the public and private key distribution task in the system. Second, there exists a spatiotemporal correlation between consecutive location-based transactions. Third, users communicate with each other through short-range communication technologies such that it constitutes a proof of location (PoL) on their actual locations. We show that, in a permissioned blockchain with an authority and a presence of a PoL, existing approaches cannot be applied using a plug-and-play approach to protect location privacy. In this context, we propose BlockPriv, an obfuscation technique that quantifies, both theoretically and experimentally, the relationship between privacy and utility in order to dynamically protect the privacy of sensitive locations in the permissioned blockchain.