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cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Auditable Versioned Data Storage Outsourcing
Ertem Esiner, Anwitaman Datta · 2015-07-31 · via cs.DS updates on arXiv.org

Auditability is crucial for data outsourcing, facilitating accountability and identifying data loss or corruption incidents in a timely manner, reducing in turn the risks from such losses. In recent years, in synch with the growing trend of outsourcing, a lot of progress has been made in designing probabilistic (for efficiency) provable data possession (PDP) schemes. However, even the recent and advanced PDP solutions that do deal with dynamic data, do so in a limited manner, and for only the latest version of the data. A naive solution treating different versions in isolation would work, but leads to tremendous overheads, and is undesirable. In this paper, we present algorithms to achieve full persistence (all intermediate configurations are preserved and are modifiable) for an optimized skip list (known as FlexList) so that versioned data can be audited. The proposed scheme provides deduplication at the level of logical, variable sized blocks, such that only the altered parts of the different versions are kept, while the persistent data-structure facilitates access (read) of any arbitrary version with the same storage and process efficiency that state-of-the-art dynamic PDP solutions provide for only the current version, while commit (write) operations incur around 5% additional time. Furthermore, the time overhead for auditing arbitrary versions in addition to the latest version is imperceptible even on a low-end server...