We present sec-cs, a hash-table-like data structure for contents on untrusted storage that is provably secure and storage-efficient. We achieve authenticity and confidentiality with zero storage overhead using deterministic authenticated encryption. State-of-the-art data deduplication approaches prevent redundant storage of shared parts of different contents irrespective of whether relationships between contents are known a priori.,,,,Instead of just adapting existing approaches, we introduce novel (multi-level) chunking strategies, ML-SC and ML-CDC, which are significantly more storage-efficient than existing approaches in presence of high redundancy.,,,,We prove sec-cs's security, publish an implementation, and present evaluation results indicating suitability for, e.g., future backup systems that should preserve many versions of files on little available storage.
Many more works exist in the field of cloud storage security. Most of them, however, have a different focus and are orthogonal to our work. Athos @cite_40 , e.g., is a solution for outsourcing file systems that achieves integrity in a way that file system operations are possible with logarithmic communication costs. The solution is orthogonal to our work in the sense that this requirement is w.r.t. the total number of files directories in the file system and not w.r.t. the size of single file contents. A similar goal is pursued by @cite_13 . Both works are based on authenticated skip lists, an authenticated data structure (ADS) initially proposed by @cite_6 , while our work is based on another ADS---the Merkle tree @cite_9 .
@cite_9 @cite_40 @cite_13 @cite_6
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Users are increasingly storing, accessing, and exchanging data through public cloud services such as those provided by Google, Facebook, Apple, and Microsoft. Although users may want to have faith in cloud providers to provide good security protection, the confidentiality of any data in public clouds can be violated, and consequently, while providers may not be "doing evil," we can not and should not trust them with data confidentiality. To better protect the privacy of user data stored in the cloud, in this paper we propose a privacy-preserving system called Mimesis Aegis (M-Aegis) that is suitable for mobile platforms. M-Aegis is a new approach to user data privacy that not only provides isolation but also preserves the user experience through the creation of a conceptual layer called Layer 7.5 (L-7.5), which is interposed between the application (OSI Layer 7) and the user (Layer 8). This approach allows M-Aegis to implement true end-to-end encryption of user data with three goals in mind: 1) complete data and logic isolation from untrusted entities; 2) the preservation of original user experience with target apps; and 3) applicable to a large number of apps and resilient to app updates. In order to preserve the exact application workflow and look-and-feel, M-Aegis uses L-7.5 to put a transparent window on top of existing application GUIs to both intercept plaintext user input before transforming the input and feeding it to the underlying app, and to reverse-transform the output data from the app before displaying the plaintext data to the user. This technique allows M-Aegis to transparently integrate with most cloud services without hindering usability and without the need for reverse engineering. We implemented a prototype of M-Aegis on Android and show that it can support a number of popular cloud services, e.g. Gmail, Facebook Messenger, WhatsApp, etc. Our performance evaluation and user study show that users incur minimal overhead when adopting M-Aegis on Android: imperceptible encryption decryption latency and a low and adjustable false positive rate when searching over encrypted data. Many proposals for managing replicated data use sites running the Snapshot Isolation (SI) concurrency control mechanism, and provide 1-copy SI or something similar, as the global isolation level. This allows good scalability, since only ww-conflicts need to be managed globally. However, 1-copy SI can lead to data corruption and violation of integrity constraints [5]. 1-copy serializability is the global correctness condition that prevents data corruption. We propose a new algorithm Replicated Serializable Snapshot Isolation (RSSI) that uses SI at each site, and combines this with a certification algorithm to guarantee 1-copy serializable global execution. Management of ww-conflicts is similar to what is done in 1-copy SI. But unlike previous designs for 1-copy serializable systems, we do not need to prevent all rw-conflicts among concurrent transactions. We formalize this in a theorem that shows that many rw-conflicts are indeed false positives that do not risk non-serializable behavior. Our proposed RSSI algorithm will only abort a transaction when it detects a well-defined pattern of two consecutive rw-edges in the serialization graph. We have built a prototype that integrates our RSSI with the existing open-source PostgresR(SI) system. Our performance evaluation shows that there is a worst-case overhead of about 15 for getting full 1copy serializability as compared to 1-copy SI in a cluster of 8 nodes, with our proposed RSSI clearly outperforming the previous work [6] for update-intensive workloads. The problem of securing data against eavesdropping in distributed storage systems is studied. The focus is on systems that use linear codes and implement exact repair to recover from node failures. The maximum file size that can be stored securely is determined for systems in which all the available nodes help in repair (i.e., repair degree d = n -1, where n is the total number of nodes) and for any number of compromised nodes. Similar results in the literature are restricted to the case of at most two compromised nodes. Moreover, new explicit upper bounds are given on the maximum secure file size for systems with d <; n - 1. The key ingredients for the contribution of this paper are new results on subspace intersection for the data downloaded during repair. The new bounds imply the interesting fact that the maximum amount of data that can be stored securely decreases exponentially with the number of compromised nodes. Whether this exponential decrease is fundamental or is a consequence of the exactness and linearity constraints remains an open question. We present the first scalable, geo-replicated storage system that guarantees low latency, offers a rich data model, and provides "stronger" semantics. Namely, all client requests are satisfied in the local datacenter in which they arise; the system efficiently supports useful data model abstractions such as column families and counter columns; and clients can access data in a causally-consistent fashion with read-only and write-only transactional support, even for keys spread across many servers. The primary contributions of this work are enabling scalable causal consistency for the complex columnfamily data model, as well as novel, non-blocking algorithms for both read-only and write-only transactions. Our evaluation shows that our system, Eiger, achieves low latency (single-ms), has throughput competitive with eventually-consistent and non-transactional Cassandra (less than 7 overhead for one of Facebook's real-world workloads), and scales out to large clusters almost linearly (averaging 96 increases up to 128 server clusters).