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Lecture notes in computer science

Conversion of Real-Numbered Privacy-Preserving Problems into the Integer Domain

Wilko Henecka, Nigel Geoffrey Bean, Matthew Roughan

Published Jan 1, 2012• 0 citations
Publisher
DOI
10.1007/978-3-642-34129-8_12

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A myriad of conditional privacy-preserving signature (CPPS) schemes have emerged recently, aiming to secure communication data while preserving user privacy. However, many of these schemes, relying on classical mathematical problems and identity based cryptographic mechanism, suffer from key escrow vulnerabilities and are susceptible to quantum attacks. In this paper, we propose a lattice based conditional privacy-preserving certificateless signature scheme. By leveraging the ring small integer solution problem and the decisional compact knapsack problem, our scheme demonstrates robust resistance against quantum attacks with high asymptotic efficiency. We utilize certificateless signature to circumvent the key escrow issue inherent in identity based CPPS schemes. While ensuring conditional privacy preservation, we enhance the security and efficiency of the signature process through a rejection sampling algorithm based on the bimodal Gaussian distribution. Furthermore, our security analysis demonstrates resilience against type I and type II adversaries, meeting diverse security requirements such as traceability and unlinkability. Through a comparative analysis of communication traffic, we observe commendable efficiency in signature generation, making our scheme particularly suitable for wireless communication scenarios. Finally, we detail implementation strategies tailored for drone networks, enhancing the practicality and relevance of our contributions in wireless communication contexts.
Quantum computing revolutionizes the way of solving complex problems and handling vast datasets, which shows great potential to accelerate the machine learning process. However, data leakage in quantum machine learning (QML) may present privacy risks. Although differential privacy (DP), which protects privacy through the injection of artificial noise, is a well-established approach, its application in the QML domain remains under-explored. In this paper, we propose to harness inherent quantum noises to protect data privacy in QML. Especially, considering the Noisy Intermediate-Scale Quantum (NISQ) devices, we leverage the unavoidable shot noise and incoherent noise in quantum computing to preserve the privacy of QML models for binary classification. We mathematically analyze that the gradient of quantum circuit parameters in QML satisfies a Gaussian distribution, and derive the upper and lower bounds on its variance, which can potentially provide the DP guarantee. Through simulations, we show that a target privacy protection level can be achieved by running the quantum circuit a different number of times.