Privacy Budgeting for Growing Machine Learning Datasets

Li, Weiting; Xiang, Liyao; Zhou, Zhou; Peng, Feng

doi:10.1109/infocom42981.2021.9488920

Cited by 10 publications

(1 citation statement)

References 13 publications

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“…Despite the enticing benefits of MLaaS, security concerns and privacy issues gradually emerge [10][11][12]. A typical machine learning service for inference on the cloud is as follows.…”

Section: Introductionmentioning

confidence: 99%

Achieving Verifiable Decision Tree Prediction on Hybrid Blockchains

Fu,

Zhang,

et al. 2023

Entropy

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Machine learning has become increasingly popular in academic and industrial communities and has been widely implemented in various online applications due to its powerful ability to analyze and use data. Among all the machine learning models, decision tree models stand out due to their great interpretability and simplicity, and have been implemented in cloud computing services for various purposes. Despite its great success, the integrity issue of online decision tree prediction is a growing concern. The correctness and consistency of decision tree predictions in cloud computing systems need more security guarantees since verifying the correctness of the model prediction remains challenging. Meanwhile, blockchain has a promising prospect in two-party machine learning services as the immutable and traceable characteristics satisfy the verifiable settings in machine learning services. In this paper, we initiate the study of decision tree prediction services on blockchain systems and propose VDT, a Verifiable Decision Tree prediction scheme for decision tree prediction. Specifically, by leveraging the Merkle tree and hash function, the scheme allows the service provider to generate a verification proof to convince the client that the output of the decision tree prediction is correctly computed on a particular data sample. It is further extended to an update method for a verifiable decision tree to modify the decision tree model efficiently. We prove the security of the proposed VDT schemes and evaluate their performance using real datasets. Experimental evaluations show that our scheme requires less than one second to produce verifiable proof.

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“…Despite the enticing benefits of MLaaS, security concerns and privacy issues gradually emerge [10][11][12]. A typical machine learning service for inference on the cloud is as follows.…”

Section: Introductionmentioning

confidence: 99%