PP-HDC: A Privacy-Preserving Inference Framework for Hyperdimensional Computing.

Design, Automation, and Test in Europe(2024)

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Recently, brain-inspired hyperdimensional computing (HDC), an emerging neuro-symbolic computing scheme that imitates human brain functions to process information using abstract and high-dimensional patterns, has seen increasing applications in multiple application domains and deployment in edge-cloud collaborative processing. However, sending sensitive data to the cloud for inference may face severe privacy threats. Unfortunately, HDC is particularly vulnerable to privacy threats due to its reversible nature. To address this challenge, we propose PP-HDC, a novel privacy-preserving inference framework for HDC. PP-HDC is designed to protect the privacy of both inference input and output. To preserve the privacy of inference input, we propose a novel hash-encoding approach in high-dimensional space by implementing a sliding-window-based transformation on the input hypervector (HV). By leveraging the unique mathematical properties of HDC, we are able to seamlessly perform training and inference on the hash-encoded HV with negligible overhead. For inference output privacy, we propose a multi-model inference approach to encrypt the inference results by leveraging the unique structure of HDC item memories and ensuring the inference result is only accessible to the owner with a proper key. We evaluate PP-HDC on three datasets and demonstrate that PP-HDC enhances privacy-preserving effects compared with state-of-the-art works while incurring minimal accuracy loss.
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Key words
Hyperdimensional Computing,Accuracy Loss,Inference Results,Memory Items,Reversible Nature,Multi-model Approach,Privacy Threats,Deep Neural Network,Cloud Computing,Image Reconstruction,Data Privacy,Search Space,Hash Function,Peak Signal-to-noise Ratio,Secret Key,True Results,Raspberry Pi,MNIST Dataset,Threat Model,Privacy Preservation,Locality Sensitive Hashing,Differential Privacy,Brute-force Attacks,Multi-party Computation,Malicious Users,Noise Injection,Accuracy Drop,Raw Input,Relative Distance
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