Dynamic Allocation for Resource Protection in Decentralized Cloud Storage Decentralized Cloud Storage (DCS) networks represent an interesting solution for data storage and management. DCS networks rely on the voluntary effort of a considerable number of (possibly untrusted) nodes, which may dynamically join and leave the network at any time. To profitably rely on DCS for data storage, data owners therefore need solutions that guarantee confidentiality and availability of their data. In this paper, we present an approach enabling data owners to keep data confidentiality and availability under control, limiting the owners intervention with corrective actions when availability or confidentiality is at risk. Our approach is based on the combined adoption of AONT (AllOrNothingTransform) and fountain codes. It provides confidentiality of outsourced data also against malicious coalitions of nodes, and guarantees data availability even in case of node failures. Our experimental evaluation clearly shows the benefits of using fountain codes with respect to other approaches adopted by current DCS networks.

Delta Encoding Overhead Analysis of Cloud Storage Systems Using ClientSide Encryption With clientside encryption (CSE), a useru0027s data is encrypted before being transferred to a cloud provider. This ensures that only the intended user has access to the information, but complicates effective file synchronization (between different devices and the cloud). Motivated by prior findings that empirically show that the largest performance differences between popular CSE services (CSEs) and nonCSEs typically are related to the implementation of delta encoding solutions to reduce bandwidth usage, in this paper, we evaluate and provide insights into the practical CSErelated delta encoding overheads. First, we use targeted experiments to demonstrate the delta encoding problem associated with CSE and to compare the practical overhead differences associated with three example services implementing delta encoding. Second, we develop an analytic cost model and use it to show that a simple thresholdbased CSE policy can reduce the bandwidth and storage usage seen by the best CSE considered here, that such a policy has a provable worstcase overhead within a factor two of the best nonCSE, and typically performs much better. The results are highly encouraging, and show that it is possible to provide CSE at limited additional overhead compared to nonCSE services.