Certificateless public auditing with data privacy preserving for cloud-based smart grid data

Abstract

As the promising next generation power system, smart grid can collect and analyze the grid information in real time, which greatly improves the reliability and efficiency of the grid. However, as smart grid coverage expands, more and more data is being collected. To store and manage the massive amount of smart grid data, the data owners choose to upload the grid data to the cloud for storage and regularly check the integrity of their data. However, traditional public auditing schemes are mostly based on Public Key Infrastructure (PKI) or Identity Based Cryptography (IBC) system, which will lead to complicated certificate management and inherent key escrow problems. We propose a certificateless public auditing scheme for cloud-based smart grid data, which can avoid the above two problems. In order to prevent the disclosure of the private data collected by the smart grid during the phase of auditing, we use the random masking technology to protect data privacy. The security analysis and the performance evaluation show that the proposed scheme is secure and efficient.

Cloud-Assisted Privacy-Preserving Spectral Clustering Algorithm Within a Multi-User Setting

“…In scenarios involving multiple users sharing data, uploading their data to cloud servers and utilizing the robust computational power of cloud computing can facilitate the integration and joint analysis of data from multiple sources. This collaborative clustering model holds the promise of improving the accuracy and robustness of clustering results, aiding data owners in better understanding the inherent structure and patterns within their data [2]- [6].…”