Digital repositories, either digital preservation systems or archival systems, periodically check the integrity of stored objects to assure users of their correctness. To do so, prior solutions calculate integrity metadata and require the repository to store it alongside the actual data objects. This integrity metadata is essential for regularly verifying the correctness of the stored data objects. To safeguard and detect damage to this metadata, prior solutions rely on widely visible media, that is unaffiliated third parties, to store and provide back digests of the metadata to verify it is intact. However, they do not address recovery of the integrity metadata in case of damage or attack by an adversary. In essence, they do not preserve this metadata. We introduce IntegrityCatalog, a system that collects all integrity related metadata in a single component, and treats them as first class objects, managing both their integrity and their preservation. We introduce a treap-based persistent authenticated dictionary managing arbitrary length key value pairs, which we use to store all integrity metadata, accessible simply by object name. Additionally, IntegrityCatalog is a distributed system that includes a network protocol that manages both corruption detection and preservation of this metadata, using administrator-selected network peers with two possible roles. Verifiers store and offer attestations on digests and have minimal storage requirements, while preservers efficiently synchronize a complete copy of the catalog to assist in recovery in case of a detected catalog compromise on the local system. We describe our prototype implementation of IntegrityCatalog, measure its performance empirically, and demonstrate its effectiveness in real-world situations, with worst measured throughput of approximately 1K insertions per second, and 2K verified search operations per second.
Our solution is orthogonal to external audits by the owner of the objects @cite_27 , or by an external auditor @cite_4 .
@cite_27 @cite_4
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The seller of N distinct objects is uncertain about the buyer’s valuation for those objects. The seller’s problem, to maximize expected revenue, consists of maximizing a linear functional over a convex set of mechanisms. A solution to the seller’s problem can always be found in an extreme point of the feasible set. We identify the relevant extreme points and faces of the feasible set. With N = 1, the extreme points are easily described providing simple proofs of well-known results. The revenue-maximizing mechanism assigns the object with probability one or zero depending on the buyer’s report. With N > 1, extreme points often involve randomization in the assignment of goods. Virtually any extreme point of the feasible set maximizes revenue for a well-behaved distribution of buyer’s valuations. We provide a simple algebraic procedure to determine whether a mechanism is an extreme point. We consider the two dimensional fully-dynamic orthogonal range reporting problem and the two dimensional fully-dynamic orthogonal line segment intersection reporting problem in the comparison model. We show that if n is the number of stored elements, then these problems can be solved in worst case time Θ(log n) plus time proportional to the size of the output pr. operation. This paper considers the problem of recovering an unknown sparse p×p matrix X from an m×m matrix Y=AXB T , where A and B are known m×p matrices with m≪p. The main result shows that there exist constructions of the sketching matrices A and B so that even if X has O(p) nonzeros, it can be recovered exactly and efficiently using a convex program as long as these nonzeros are not concentrated in any single row column of X. Furthermore, it suffices for the size of Y (the sketch dimension) to scale as m = O(√(# nonzeros in X) × log p). The results also show that the recovery is robust and stable in the sense that if X is equal to a sparse matrix plus a perturbation, then the convex program we propose produces an approximation with accuracy proportional to the size of the perturbation. Unlike traditional results on sparse recovery, where the sensing matrix produces independent measurements, our sensing operator is highly constrained (it assumes a tensor product structure). Therefore, proving recovery guarantees require nonstandard techniques. Indeed, our approach relies on a novel result concerning tensor products of bipartite graphs, which may be of independent interest. This problem is motivated by the following application, among others. Consider a p×n data matrix D, consisting of n observations of p variables. Assume that the correlation matrix X:=DD T is (approximately) sparse in the sense that each of the p variables is significantly correlated with only a few others. Our results show that these significant correlations can be detected even if we have access to only a sketch of the data S=AD with A ∈ R m×p . A new method for correction of MRI motion artifacts induced by corrupted k-space data, acquired by multiple receiver coils such as phased arrays, is presented. In our approach, a projections onto convex sets (POCS)-based method for reconstruction of sensitivity encoded MRI data (POCSENSE) is employed to identify corrupted k-space samples. After the erroneous data are discarded from the dataset, the artifact-free images are restored from the remaining data using coil sensitivity profiles. The error detection and data restoration are based on informational redundancy of phased-array data and may be applied to full and reduced datasets. An important advantage of the new POCS-based method is that, in addition to multicoil data redundancy, it can use a priori known properties about the imaged object for improved MR image artifact correction. The use of such information was shown to improve significantly k-space error detection and image artifact correction. The method was validated on data corrupted by simulated and real motion such as head motion and pulsatile flow. Magn Reson Med 63:1104–1110, 2010. V C 2010