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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Privacy-Preserving Decision-Tree Evaluation with Low Complexity for Communication.

Yidi Hao1, Baodong Qin1, Yitian Sun1

  • 1School of Cyberspace Security, Xi'an University of Posts and Telecommunications, Xi'an 710121, China.

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|March 11, 2023
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Summary
This summary is machine-generated.

This study introduces a novel secure classification protocol using fully homomorphic encryption. The new method significantly reduces communication costs and offers quantum resistance for machine learning services.

Keywords:
decisional treefully homomorphic encryptionsecure integer comparison

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Area of Science:

  • Cryptography
  • Machine Learning Security
  • Privacy-Preserving Computation

Background:

  • Companies offer machine learning services, raising concerns about user data and model privacy.
  • Existing privacy solutions are communication-intensive and vulnerable to quantum attacks.

Purpose of the Study:

  • To design a secure integer-comparison protocol and a client-server classification protocol for decision-tree evaluation.
  • To develop a privacy-preserving, quantum-resistant, and communication-efficient machine learning classification method.

Main Methods:

  • Developed a secure integer-comparison protocol utilizing fully homomorphic encryption.
  • Proposed a client-server decision-tree classification protocol based on the secure integer-comparison protocol.
  • Implemented a lattice-based fully homomorphic encryption scheme resistant to quantum attacks.

Main Results:

  • The proposed classification protocol achieves a low communication cost, requiring only one round of user interaction.
  • Experimental results demonstrate a communication cost reduction to 20% compared to traditional methods.
  • The protocol is built on a quantum-resistant lattice-based fully homomorphic encryption scheme.

Conclusions:

  • The novel protocol offers a secure, efficient, and quantum-resistant solution for privacy-preserving machine learning classification.
  • This approach significantly lowers communication overhead in client-server machine learning interactions.
  • The use of lattice-based fully homomorphic encryption provides a robust defense against future quantum computing threats.