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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
CacheShuffle: An Oblivious Shuffle Algorithm Using Caches
Sarvar Patel, Giuseppe Persiano, Kevin Yeo · 2017-05-20 · via cs.DS updates on arXiv.org

We consider Oblivious Shuffling and K-Oblivious Shuffling, a refinement thereof. We provide efficient algorithms for both and discuss their application to the design of Oblivious RAM. The task of K-Oblivious Shuffling is to obliviously shuffle N encrypted blocks that have been randomly allocated on the server in such a way that an adversary learns nothing about the new allocation of blocks. The security guarantee should hold also with respect to an adversary that has learned the initial position of K touched blocks out of the N blocks. The classical notion of Oblivious Shuffling is obtained for K = N. We present a family of algorithms for Oblivious Shuffling. Our first construction, CacheShuffleRoot, is tailored for clients with $O(\sqrt{N})$ blocks of memory and uses $(4+ε)N$ blocks of bandwidth, for every $ε> 0$. CacheShuffleRoot is a 4.5x improvement over previous best known results on practical sizes of N. We also present CacheShuffle that obliviously shuffles using O(S) blocks of client memory with $O(N\log_S N)$ blocks of bandwidth. We then turn to K-Oblivious Shuffling and give algorithms that require 2N + f(K) blocks of bandwidth, for some function f. That is, any extra bandwidth above the 2N lower bound depends solely on K. We present KCacheShuffleBasic that uses O(K) client storage and exactly 2N blocks of bandwidth. For smaller client storage requirements, we show KCacheShuffle, which uses O(S) client storage and requires $2N+(1+ε)O(K\log_S K)$ blocks of bandwidth. Finally, we consider the case in which, in addition to the N blocks, the server stores D dummy blocks whose content is is irrelevant but still their positions must be hidden by the shuffling. For this case, we design algorithm KCacheShuffleDummy that, for N + D blocks and K touched blocks, uses O(K) client storage and $D+(2+ε)N$ blocks of bandwidth.