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Differential Privacy Amplification in Quantum and Quantum-inspired Algorithms

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Abstract

Differential privacy provides a theoretical framework for processing a dataset about n users, in a way that the output reveals a minimal information about any single user. Such notion of privacy is usually ensured by noise-adding mechanisms and amplified by several processes, including subsampling, shuffling, iteration, mixing and diffusion. In this work, we provide privacy amplification bounds for quantum and quantum-inspired algorithms. In particular, we show for the first time, that algorithms running on quantum encoding of a classical dataset or the outcomes of quantum-inspired classical sampling, amplify differential privacy. Moreover, we prove that a quantum version of differential privacy is amplified by the composition of quantum channels, provided that they satisfy some mixing conditions.
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Dates and versions

hal-03857573 , version 1 (17-11-2022)

Identifiers

  • HAL Id : hal-03857573 , version 1

Cite

Armando Angrisani, Mina Doosti, Elham Kashefi. Differential Privacy Amplification in Quantum and Quantum-inspired Algorithms. 2022. ⟨hal-03857573⟩
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