VeriSplit: Secure and Practical Offloading of Machine Learning Inferences across IoT Devices

• URL: http://arxiv.org/abs/2406.00586v1
• Date: Sun, 2 Jun 2024 01:28:38 GMT
• Title: VeriSplit: Secure and Practical Offloading of Machine Learning Inferences across IoT Devices
• Authors: Han Zhang, Zifan Wang, Mihir Dhamankar, Matt Fredrikson, Yuvraj Agarwal,
• Abstract summary: Many Internet-of-Things (IoT) devices rely on cloud computation resources to perform machine learning inferences. This is expensive and may raise privacy concerns for users. We propose VeriSplit, a framework for offloading machine learning inferences to locally-available devices.
• Score: 31.247069150077632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many Internet-of-Things (IoT) devices rely on cloud computation resources to perform machine learning inferences. This is expensive and may raise privacy concerns for users. Consumers of these devices often have hardware such as gaming consoles and PCs with graphics accelerators that are capable of performing these computations, which may be left idle for significant periods of time. While this presents a compelling potential alternative to cloud offloading, concerns about the integrity of inferences, the confidentiality of model parameters, and the privacy of users' data mean that device vendors may be hesitant to offload their inferences to a platform managed by another manufacturer. We propose VeriSplit, a framework for offloading machine learning inferences to locally-available devices that address these concerns. We introduce masking techniques to protect data privacy and model confidentiality, and a commitment-based verification protocol to address integrity. Unlike much prior work aimed at addressing these issues, our approach does not rely on computation over finite field elements, which may interfere with floating-point computation supports on hardware accelerators and require modification to existing models. We implemented a prototype of VeriSplit and our evaluation results show that, compared to performing computation locally, our secure and private offloading solution can reduce inference latency by 28%--83%.

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