Cloud business intelligence is an increasingly popular choice to deliver decision support capabilities via elastic, pay-per-use resources. However, data security issues are one of the top concerns when dealing with sensitive data. In this paper, we propose a novel approach for securing cloud data warehouses by flexible verifiable secret sharing, fVSS. Secret sharing encrypts and distributes data over several cloud service providers, thus enforcing data privacy and availability. fVSS addresses four shortcomings in existing secret sharing-based approaches. First, it allows refreshing the data warehouse when some service providers fail. Second, it allows on-line analysis processing. Third, it enforces data integrity with the help of both inner and outer signatures. Fourth, it helps users control the cost of cloud warehousing by balancing the load among service providers with respect to their pricing policies. To illustrate fVSS' efficiency, we thoroughly compare it with existing secret sharing-based approaches with respect to security features, querying power and data storage and computing costs.
In the first family of approaches @cite_15 @cite_7 @cite_6 @cite_1 @cite_3 , each table is encrypted into @math shared tables, each of which is stored at one given CSP (Figure ). Recall that only @math of @math shared tables are sufficient to reconstruct the original table. Most of these approaches assume that CSPs are not malicious and that connections between CSPs and users are secure. Only one @cite_7 includes a data verification process that exploits hash-generated signatures: an inner signature (incorporated to the shares) to verify whether CSPs are malicious, and an outer signature that helps detect incorrect or erroneous (lost, damaged, alternative...) data before decryption and prevents useless data transfers. Both signatures are stored at CSPs.
@cite_7 @cite_1 @cite_3 @cite_6 @cite_15
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The emerging use of the Internet for remote storage and backup has led to the problem of verifying that storage sites in a distributed system indeed store the data; this must often be done in the absence of knowledge of what the data should be. We use m n erasure-correcting coding to safeguard the stored data and use algebraic signatures hash functions with algebraic properties for verification. Our scheme primarily utilizes one such algebraic property: taking a signature of parity gives the same result as taking the parity of the signatures. To make our scheme collusionresistant, we blind data and parity by XORing them with a pseudo-random stream. Our scheme has three advantages over existing techniques. First, it uses only small messages for verification, an attractive property in a P2P setting where the storing peers often only have a small upstream pipe. Second, it allows verification of challenges across random data without the need for the challenger to compare against the original data. Third, it is highly resistant to coordinated attempts to undetectably modify data. These signature techniques are very fast, running at tens to hundreds of megabytes per second. Because of these properties, the use of algebraic signatures will permit the construction of large-scale distributed storage systems in which large amounts of storage can be verified with minimal network bandwidth. Users of content-based publish subscribe systems (CBPS) are interested in receiving data items with values that satisfy certain conditions. Each user submits a list of subscription specifications to a broker, which routes data items from publishers to users. When a broker receives a notification that contains a value from a publisher, it forwards it only to the subscribers whose requests match the value. However, in many applications, the data published are confidential, and their contents must not be revealed to brokers. Furthermore, a user's subscription may contain sensitive information that must be protected from brokers. Therefore, a difficult challenge arises: how to route publisher data to the appropriate subscribers without the intermediate brokers learning the plain text values of the notifications and subscriptions. To that extent, brokers must be able to perform operations on top of the encrypted contents of subscriptions and notifications. Such operations may be as simple as equality match, but often require more complex operations such as determining inclusion of data in a value interval. Previous work attempted to solve this problem by using one-way data mappings or specialized encryption functions that allow evaluation of conditions on ciphertexts. However, such operations are computationally expensive, and the resulting CBPS lack scalability. As fast dissemination is an important requirement in many applications, we focus on a new data transformation method called Asymmetric Scalar-product Preserving Encryption (ASPE) [1]. We devise methods that build upon ASPE to support private evaluation of several types of conditions. We also suggest techniques for secure aggregation of notifications, supporting functions such as sum, minimum, maximum and count. Our experimental evaluation shows that ASPE-based CBPS incurs 65 less overhead for exact-match filtering and 50 less overhead for range filtering compared to the state-of-the-art. This paper describes a cryptographic protocol for securing self-organized data storage through periodic verifications. The proposed verification protocol, which goes beyond simple integrity checks and proves data conservation, is deterministic, efficient, and scalable. The security of this scheme relies both on the ECDLP intractability assumption and on the difficulty of finding the order of some specific elliptic curve over Zn. The protocol also makes it possible to personalize replicas and to delegate verification without revealing any secret information. Restoration of writing order from a single-stroked handwriting image can be seen as the problem of finding the smoothest path in its graph representation. In this paper, a 3-phase approach to restore a writing order is proposed within the framework of the edge continuity relation (ECR). In the initial, local phase, in order to obtain possible ECRs at an even-degree node, a neural network is used for the node of degree 4 and a theoretical approach is presented for the node of degree higher than 4 by introducing certain reasonable assumptions. In the second phase, we identify double-traced lines by employing maximum weighted matching. This makes it possible to transform the problem of obtaining possible ECRs at odd-degree node to that at even-degree node. In the final, global phase, we find all the candidates of single-stroked paths by depth first search and select the best one by evaluating SLALOM smoothness. Experiments on static images converted from online data in the Unipen database show that our method achieves a restoration rate of 96.0 percent