FPMDA: Fault-Tolerant and Privacy-Enhanced Multidimensional Data Aggregation Without TA

Many privacy-preserving multidimensional data aggregation (PPMDA) schemes have been proposed to safeguard user privacy and provide aggregated real-time data for the control center (CC) to optimize power allocation in the smart grid. However, without Trusted Authority (TA), existing PPMDA schemes cannot simultaneously provide lightweight encryption, achieve fault tolerance, and resist collusion attacks between fog nodes and CC. To address these issues, we propose a fault-tolerant and privacy-enhanced multidimensional data aggregation scheme without TA (FPMDA) based on fog computing. Specifically, the Chinese Remainder Theorem is leveraged to enhance the efficiency of multidimensional data processing, and an innovative dual-masking approach is introduced to ensure the security of data aggregation. In addition, employing the homomorphic property of the (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t, k</i>)-threshold secret sharing algorithm, we design a data aggregation method that enhances security and fault tolerance, making it resilient against insider attacks. Finally, compared with existing schemes, FPMDA not only significantly enhances privacy preservation while maintaining required security properties but also achieves low computational and communication load, demonstrating practicality for resource-constrained smart meters.