Efficient dynamic multi-replica auditing for the cloud with geographic location

Efficient Data Integrity Auditing Supporting Provable Data Update for Secure Cloud Storage

“…Meanwhile, they all utilize secure encryption algorithms to prevent privacy information from leakage, thus ensuring data confidentiality. Second, our designed scheme and the previous scheme [33] can achieve block-based data update, but the previous scheme [16] cannot support data update. Finally, the previous scheme [33] requires relying on a TA to realize data integrity checking and provable data update, while the other two solutions do not need to interact with a TA.…”

“…We would compare the functionality of our designed scheme and two previous schemes [16,33], as demonstrated in Table 1.…”

“…Finally, the previous scheme [33] requires relying on a TA to realize data integrity checking and provable data update, while the other two solutions do not need to interact with a TA. Therefore, we can trust that the overall functionalities of our designed scheme are more appealing than the other two previous solutions [16,33].…”

“…Moreover, symbol n represents the number of outsourced subfiles, symbol P represents bilinear pairings computation, symbol k denotes the number of audited data blocks, and symbol q denotes the number of updated data blocks. In scheme [33], we assume each data block would be further split into s sectors.…”

“…In their protocol, the modular exponentiation was replaced by the vector dot products, thus effectively saving computational resources. In 2021, Yu et al [33] used indexed Merkle hash tree (IMHT) to design an efficient dynamic data auditing protocol, which could support fully dynamic operations. To achieve batch verification, Guo et al [34] utilized implicitly indexed balanced Merkle tree (IBMT) to put forward a dynamic proof of data possession and replication (DPDPR) protocol.…”