DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive

Weng, Jiasi; Zhang, Jilian; Li, Ming; Zhang, Yue; Luo, Weiqi

doi:10.1109/tdsc.2019.2952332

Cited by 365 publications

(223 citation statements)

References 40 publications

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“…We propose more research in pre-computing as much as possible off-chain and only performing necessary steps in a smart contract. Techniques such as fine-tuning (in the transfer learning field) [23] or off-chain training as proposed for DeepChain [24] can help. One can use a common encoder (shared in more conventional ways such as via web APIs or publicly posting source code to a website) off-chain to encode the input and then fine-tune the encoded result on-chain with a simple neural network or even just a single layer.…”

Section: A Modelsmentioning

confidence: 99%

Decentralized and Collaborative AI on Blockchain

Harris¹,

Waggoner²

2019

2019 IEEE International Conference on Blockchain (Blockchain)

106

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Machine learning has recently enabled large advances in artificial intelligence, but these tend to be highly centralized. The large datasets required are generally proprietary; predictions are often sold on a per-query basis; and published models can quickly become out of date without effort to acquire more data and re-train them. We propose a framework for participants to collaboratively build a dataset and use smart contracts to host a continuously updated model. This model will be shared publicly on a blockchain where it can be free to use for inference. Ideal learning problems include scenarios where a model is used many times for similar input such as personal assistants, playing games, recommender systems, etc. In order to maintain the model's accuracy with respect to some test set we propose both financial and non-financial (gamified) incentive structures for providing good data. A free and open source implementation for the Ethereum blockchain is provided at https://github.com/microsoft/0xDeCA10B.

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Section: A Modelsmentioning

confidence: 99%

Decentralized and Collaborative AI on Blockchain

Harris¹,

Waggoner²

2019

2019 IEEE International Conference on Blockchain (Blockchain)

106

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“…They reviewed the intersection of blockchains and intrusion-detection systems and identified open challenges. DeepChain by Weng et al [27] specifically focuses on federated learning and privacy-preserving deep learning. They addressed a threat scenario including malicious participants that may damage the correctness of training, a concern also relevant in our work.…”

Section: Machine Learning and Blockchainsmentioning

confidence: 99%

“…Rather than learning an anomaly detection model and evaluating it on a single high-end machine (as done in DeepChain [27]), we partition and distribute the CICIDS2017 dataset on a real network of desktop nodes and resource-constrained devices. They all will participate in the federated learning setup.…”

Section: Anomaly Detection Through Federated Learningmentioning

confidence: 99%

Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

et al. 2018

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The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions. However, adversaries too are becoming more effective in concealing malicious behavior amongst large amounts of benign behavior data. To address the increasing time-to-detection of these stealthy attacks, interconnected and federated learning systems can improve the detection of malicious behavior by joining forces and pooling together monitoring data. The major challenge that we address in this work is that in a federated learning setup, an adversary has many more opportunities to poison one of the local machine learning models with malicious training samples, thereby influencing the outcome of the federated learning and evading detection. We present a solution where contributing parties in federated learning can be held accountable and have their model updates audited. We describe a permissioned blockchain-based federated learning method where incremental updates to an anomaly detection machine learning model are chained together on the distributed ledger. By integrating federated learning with blockchain technology, our solution supports the auditing of machine learning models without the necessity to centralize the training data. Experiments with a realistic intrusion detection use case and an autoencoder for anomaly detection illustrate that the increased complexity caused by blockchain technology has a limited performance impact on the federated learning, varying between 5 and 15%, while providing full transparency over the distributed training process of the neural network. Furthermore, our blockchain-based federated learning solution can be generalized and applied to more sophisticated neural network architectures and other use cases.

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“…Neureal is a similar effort [12] that allows idle computing power to be commoditized and used for analyzing big data, while rewarding miners for the accuracy of their predictions. Similarly, Deepchain [13] attempts to achieve privacy-preserving model training, wherein mistrustful parties are given incentives to participate in federated learning by sharing gradients and correctly updating parameters. The Ocean protocol [14] is a blockchain based decentralized data exchange protocol, which connects data owners with data consumers (i.e.…”

Section: A Related Workmentioning

confidence: 99%

Blockchain Enabled Trustless API Marketplace

Arya¹,

Sen²,

Kodeswaran³

2020

2020 International Conference on COMmunication Systems &Amp; NETworkS (COMSNETS)

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There has been an unprecedented surge in the number of service providers offering a wide range of machine learning prediction APIs for tasks such as image classification, language translation, etc. thereby monetizing the underlying data and trained models. Typically, a data owner (API provider) develops a model, often over proprietary data, and leverages the infrastructure services of a cloud vendor for hosting and serving API requests. Clearly, this model assumes complete trust between the API Provider and cloud vendor. On the other hand, a malicious/buggy cloud vendor may copy the APIs and offer an identical service, under-report model usage metrics, or unfairly discriminate between different API providers by offering them a nominal share of the revenue. In this work, we present the design of a blockchain based decentralized trustless API marketplace that enables all the stakeholders in the API ecosystem to audit the behavior of the parties without having to trust a single centralized entity. In particular, our system divides an AI model into multiple pieces and deploys them among multiple cloud vendors who then collaboratively execute the APIs. Our design ensures that cloud vendors cannot collude with each other to steal the combined model, while individual cloud vendors and clients cannot repudiate their input or model executions.Index Terms-Innovative use cases which leverage blockchain, AI, Model inference, Software as a service API

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DeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive

Cited by 365 publications

References 40 publications

Decentralized and Collaborative AI on Blockchain

Decentralized and Collaborative AI on Blockchain

Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

Blockchain Enabled Trustless API Marketplace

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