Differential Privacy Amplification in Quantum and Quantum-inspired Algorithms

• URL: http://arxiv.org/abs/2203.03604v2
• Date: Mon, 21 Aug 2023 22:31:18 GMT
• Title: Differential Privacy Amplification in Quantum and Quantum-inspired Algorithms
• Authors: Armando Angrisani, Mina Doosti and Elham Kashefi
• Abstract summary: We provide privacy bounds amplification for quantum and quantum-inspired algorithms. We show for the first time, that algorithms running on quantum encoding of a classical dataset amplify differential privacy.
• Score: 0.6827423171182154
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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