Purdue University is a public research university in Indiana, known for top programs in engineering, aerospace, and data science. It supports innovation and entrepreneurship across academia and industry. NEC Labs America partners with Purdue University on federated analytics, interpretability, and privacy-preserving inference. Our joint work helps advance trusted machine learning pipelines. Please read about our latest news and collaborative publications with Purdue University.

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CamTuner: Reinforcement Learning based System for Camera Parameter Tuning to enhance Analytics

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Video analytics systems critically rely on video cameras, which capture high quality video frames, to achieve high analytics accuracy. Although modern video cameras often expose tens of configurable parameter settings that can be set by end users, deployment of surveillance cameras today often uses a fixed set of parameter settings because the end users lack the skill or understanding to reconfigure these parameters. In this paper, we first show that in a typical surveillance camera deployment, environmental condition changes can significantly affect the accuracy of analytics units such as person detection, face detection and face recognition, and how such adverse impact can be mitigated by dynamically adjusting camera settings. We then propose CAMTUNER, a framework that can be easily applied to an existing video analytics pipeline (VAP) to enable automatic and dynamic adaptation of complex camera settings to changing environmental conditions, and autonomously optimize the accuracy of analytics units (AUs) in the VAP. CAMTUNER is based on SARSA reinforcement learning (RL) and it incorporates two novel components: a light weight analytics quality estimator and a virtual camera. CAMTUNER is implemented in a system with AXIS surveillance cameras and several VAPs (with various AUs) that processed day long customer videos captured at airport entrances. Our evaluations show that CAMTUNER can adapt quickly to changing environments. We compared CAMTUNER with two alternative approaches where either static camera settings were used, or a strawman approach where camera settings were manually changed every hour (based on human perception of quality). We observed that for the face detection and person detection AUs, CAMTUNER is able to achieve up to 13.8% and 9.2% higher accuracy, respectively, compared to the best of the two approaches (average improvement of 8% for both AUs).

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VESSELS: Efficient and Scalable Deep Learning Prediction on Trusted Processors

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Deep learning systems on the cloud are increasingly targeted by attacks that attempt to steal sensitive data. Intel SGX has been proven effective to protect the confidentiality and integrity of such data during computation. However, state-of-the-art SGX systems still suffer from substantial performance overhead induced by the limited physical memory of SGX. This limitation significantly undermines the usability of deep learning systems due to their memory-intensive characteristics.In this paper, we provide a systematic study on the inefficiency of the existing SGX systems for deep learning prediction with a focus on their memory usage. Our study has revealed two causes of the inefficiency in the current memory usage paradigm: large memory allocation and low memory reusability. Based on this insight, we present Vessels, a new system that addresses the inefficiency and overcomes the limitation on SGX memory through memory usage optimization techniques. Vessels identifies the memory allocation and usage patterns of a deep learning program through model analysis and creates a trusted execution environment with an optimized memory pool, which minimizes the memory footprint with high memory reusability. Our experiments demonstrate that, by significantly reducing the memory footprint and carefully scheduling the workloads, Vessels can achieve highly efficient and scalable deep learning prediction while providing strong data confidentiality and integrity with SGX.

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Decentralized Transactive Energy Auctions with Bandit Learning

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The power systems worldwide have been embracing the rapid growth of distributed energy resources. Commonly, distributed energy resources exist in the distribution level, such as electric vehicles, rooftop photovoltaic panels, and home battery systems, which cannot be controlled by a centralized entity like a utility. However, a large number of distributed energy resources have potential to reshape the power generation landscape when the owners (prosumers) are allowed to send electricity back to the grids. Transactive energy paradigms are emerging for orchestrating the coordination of prosumers and consumers by enabling the exchange of energy among them. In this paper, we propose a transactive energy auction framework based on blockchain technology for creating trustworthy and transparent transactive environments in distribution networks, which does not rely on a centralized entity to clear transactions. Moreover, we propose intelligent decentralized decision-making strategies by bandit learning for market participants to locally decide their energy prices in auctions. The bandit learning approach can provide market participants with more benefits under the blockchain framework than trading energy with the centralized entity, which is further supported by the preliminary simulated results conducted over our blockchain-based platform.

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PoLPer: Process-Aware Restriction of Over-Privileged Setuid Calls in Legacy Applications

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Setuid system calls enable critical functions such as user authentications and modular privileged components. Such operations must only be executed after careful validation. However, current systems do not perform rigorous checks, allowing exploitation of privileges through memory corruption vulnerabilities in privileged programs. As a solution, understanding which setuid system calls can be invoked in what context of a process allows precise enforcement of least privileges. We propose a novel comprehensive method to systematically extract and enforce least privilege of setuid system calls to prevent misuse. Our approach learns the required process contexts of setuid system calls along multiple dimensions: process hierarchy, call stack, and parameter in a process-aware way. Every setuid system call is then restricted to the per-process context by our kernel-level context enforcer. Previous approaches without process-awareness are too coarse-grained to control setuid system calls, resulting in over-privilege. Our method reduces available privileges even for identical code depending on whether it is run by a parent or a child process. We present our prototype called PoLPer which systematically discovers only required setuid system calls and effectively prevents real-world exploits targeting vulnerabilities of the setuid family of system calls in popular desktop and server software at near zero overhead.

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