Cloud storage services enable individuals and organizations to outsource data storage to remote servers. Cloud storage providers generally adopt data deduplication, a technique for eliminating redundant data by keeping only a single copy of a file, thus saving a considerable amount of storage and bandwidth. However, an attacker can abuse deduplication protocols to steal information. For example, an attacker can perform the duplicate check to verify whether a file (e.g., a pay slip, with a specific name and salary amount) is already stored (by someone else), hence breaching the user privacy. In this paper, we propose ZEUS (zero-knowledge deduplication response) framework. We develop ZEUS and ZEUS + , two privacy-aware deduplication protocols: ZEUS provides weaker privacy guarantees while being more efficient in the communication cost, while ZEUS + guarantees stronger privacy properties, at an increased communication cost. To the best of our knowledge, ZEUS is the first solution which addresses two-side privacy by neither using any extra hardware nor depending on heuristically chosen parameters used by the existing solutions, thus reducing both cost and complexity of the cloud storage. In summary, through the evaluation on real datasets and comparison to existing solutions, our proposed framework demonstrates its capability of eliminating data deduplication-based side channel and at the same time keeping the deduplication benefits.

Blockchain promises to provide a distributed and decentralized means of trust among untrusted users. However, in recent years, a shift from decentrality to centrality has been observed in the most accepted Blockchain system, i.e., Bitcoin. This shift has motivated researchers to identify the cause of decentrality, quantify decentrality and analyze the impact of decentrality. In this work, we take a holistic approach to identify and quantify decentrality in Blockchain based systems. First, we identify the emergence of centrality in three layers of Blockchain based systems, namely governance layer, network layer and storage layer. Then, we quantify decentrality in these layers using various metrics. At the governance layer, we measure decentrality in terms of fairness, entropy, Gini coefficient, Kullback-Leibler divergence, etc. Similarly, in the network layer, we measure decentrality by using degree centrality, betweenness centrality and closeness centrality. At the storage layer, we apply a distribution index to define centrality. Subsequently, we evaluate the decentrality in Bitcoin and Ethereum networks and discuss our observations. We noticed that, with time, both Bitcoin and Ethereum networks tend to behave like centralized systems where a few nodes govern the whole network.

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