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Register a new userAirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning
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Added by
Yusuke Koda
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Wireless channels can be inherently privacy-preserving by distorting the received signals due to channel noise, and superpositioning multiple signals over-the-air. By harnessing these natural distortions and superpositions by wireless channels, we propose a novel privacy-preserving machine learning (ML) framework at the network edge, coined over-the-air mixup ML (AirMixML). In AirMixML, multiple workers transmit analog-modulated signals of their private data samples to an edge server who trains an ML model using the received noisy-and superpositioned samples. AirMixML coincides with model training using mixup data augmentation achieving comparable accuracy to that with raw data samples. From a privacy perspective, AirMixML is a differentially private (DP) mechanism limiting the disclosure of each workers private sample information at the server, while the workers transmit power determines the privacy disclosure level. To this end, we develop a fractional channel-inversion power control (PC) method, {alpha}-Dirichlet mixup PC (DirMix({alpha})-PC), wherein for a given global power scaling factor after channel inversion, each workers local power contribution to the superpositioned signal is controlled by the Dirichlet dispersion ratio {alpha}. Mathematically, we derive a closed-form expression clarifying the relationship between the local and global PC factors to guarantee a target DP level. By simulations, we provide DirMix({alpha})-PC design guidelines to improve accuracy, privacy, and energy-efficiency. Finally, AirMixML with DirMix({alpha})-PC is shown to achieve reasonable accuracy compared to a privacy-violating baseline with neither superposition nor PC.
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As machine learning becomes a practice and commodity, numerous cloud-based services and frameworks are provided to help customers develop and deploy machine learning applications. While it is prevalent to outsource model training and serving tasks in the cloud, it is important to protect the privacy of sensitive samples in the training dataset and prevent information leakage to untrusted third parties. Past work have shown that a malicious machine learning service provider or end user can easily extract critical information about the training samples, from the model parameters or even just model outputs. In this paper, we propose a novel and generic methodology to preserve the privacy of training data in machine learning applications. Specifically we introduce an obfuscate function and apply it to the training data before feeding them to the model training task. This function adds random noise to existing samples, or augments the dataset with new samples. By doing so sensitive information about the properties of individual samples, or statistical properties of a group of samples, is hidden. Meanwhile the model trained from the obfuscated dataset can still achieve high accuracy. With this approach, the customers can safely disclose the data or models to third-party providers or end users without the need to worry about data privacy. Our experiments show that this approach can effective defeat four existing types of machine learning privacy attacks at negligible accuracy cost.
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