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"Enabling Efficient and Malicious Secure Data Aggregation in Smart Grid" by Haolin Pang, Kai He et al.

As the next-generation power grid, the smart grid has significantly improved dependability, flexibility, and efficiency compared with the traditional power grid. However, due to increasingly diverse application requirements, it faces challenges on balancing data privacy, efficiency, and robustness. In this paper, we present a fog computing-based smart grid model. In addition, based on the proposed model, we construct an efficient and privacy-preserving scheme that supports malicious secure smart grid usage data aggregation communication. To our best knowledge, this is the first concrete smart grid solution that concurrently achieves secure aggregation communication, data privacy, and data robustness (e.g., false data detection). Specifically, benefiting from Boolean/Arithmetic secret-sharing methods, our proposed scheme allows home users to report their electricity usage data to the cloud and fogs securely. Besides, a false data detection protocol is proposed to resist false data injec ....

Cloud Computing , Computational Modeling , Data Aggregation , Data Privacy , Alse Data Detection , Alicious Secure , Privacy Preserving , Smart Grid , Smart Grids , Smart Meters ,

Unlocking Growth Potential: How TruOps Transformed Risk Management for a Century-Old Corporation

A globally renowned manufacturer and seller of consumer lawn and gardening products implemented TruOps to ensure maximum security and efficiency, achieve a ....

Gold Corporation , Unlocking Growth Potential , How Truops Transformed Risk Management , Data Aggregation , Security Tools , Sdg Corp ,