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This summary is machine-generated.

This study introduces an efficient data aggregation scheme for smart grids, enabling personalized privacy protection for electricity data. The method balances user privacy needs with accurate grid analytics, outperforming existing approaches.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Cybersecurity

Background:

  • Smart grids enhance efficiency but raise privacy concerns for user electricity data.
  • Existing privacy methods fail to meet individual needs and reduce data accuracy.
  • A conflict exists between personalized privacy and precise grid analytics.

Purpose of the Study:

  • To propose an efficient data aggregation scheme for smart grids based on personalized local differential privacy (EDAS-PLDP).
  • To address the trade-off between individualized privacy protection and high-precision grid data analytics.
  • To enable users to select custom privacy levels for their electricity consumption data.

Main Methods:

  • Developed the efficient data aggregation scheme based on personalized local differential privacy (EDAS-PLDP).
  • Implemented a mean square error-based weighted aggregation strategy to mitigate accuracy loss.
  • Evaluated data quality from different privacy preference groups to optimize global mean estimation.

Main Results:

  • EDAS-PLDP achieves higher estimation accuracy compared to existing schemes across various privacy preferences, user scales, and data granularities.
  • The proposed scheme demonstrates lower time consumption, suitable for resource-constrained smart grids.
  • EDAS-PLDP exhibits excellent robustness against false data injection attacks.

Conclusions:

  • EDAS-PLDP offers a balanced and efficient solution for smart grids.
  • The scheme effectively reconciles personalized privacy protection with high-precision data utility.
  • It provides a practical approach for enhancing both privacy and data analytics in smart grids.