Path Oblivious Heap: Optimal and Practical Oblivious Priority Queue

Shi, Elaine

doi:10.1109/sp40000.2020.00037

Cited by 16 publications

(18 citation statements)

References 36 publications

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“…However, it is to be observed that prior ( log )-work oblivious algorithms for sorting -the AKS network [2], Zigzag sort [38], and an ( log ) version of oblivious bucket sort [4] all use expanders within their construction. Sorting can be performed using an efficient oblivious priority queue that does not use expanders [42,51]; however this incurs ( log ) work to achieve a negligible in failure probability. Further, this method is not cache-efficient and is inherently sequential.…”

Section: Our Resultsmentioning

confidence: 99%

Data Oblivious Algorithms for Multicores

Ramachandran

Shi

2021

Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures

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A data-oblivious algorithm is an algorithm whose memory access pattern is independent of the input values. We initiate the study of parallel data oblivious algorithms on realistic multicores, best captured by the binary fork-join model of computation. We present a data-oblivious CREW binary fork-join sorting algorithm with optimal total work and optimal (cache-oblivious) cache complexity, and in (log log log ) span (i.e., parallel time); these bounds match the best-known bounds for binary fork-join cache-efficient insecure algorithms. Using our sorting algorithm as a core primitive, we show how to data-obliviously simulate general PRAM algorithms in the binary fork-join model with non-trivial efficiency, and we present data-oblivious algorithms for several applications including list ranking, Euler tour, tree contraction, connected components, and minimum spanning forest. All of our data oblivious algorithms have bounds that either match or improve over the best known bounds for insecure algorithms.Complementing these asymptotically efficient results, we present a practical variant of our sorting algorithm that is self-contained and potentially implementable. It has optimal caching cost, and it is only a log log factor off from optimal work and about a log factor off in terms of span. We also present an EREW variant with optimal work and caching cost, and with the same asymptotic span.

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Section: Our Resultsmentioning

confidence: 99%

Data Oblivious Algorithms for Multicores

Ramachandran

Shi

2021

Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures

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“…Further, we find that perception blinding is highly effective in mitigation of manifold learning-based SCA. We also demonstrate the noise resiliency of our attack, and how oblivious RAM [42,97] can mitigate our attack, though it incurs high cost and becomes impractical in real-life usage. In summary, this thorough study makes the following contributions:…”

Section: Introductionmentioning

confidence: 91%

“…That said, oblivious RAM (ORAM) [42] conceal memory access sequences of a program by continuously shuffling data as they are accessed. We study whether a representative ORAM, PathOHeap [97], can mitigate our attack. Due to the limited space, we report the key evaluation results in Table 13.…”

Section: Mitigation Using Orammentioning

confidence: 99%

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Automated Side Channel Analysis of Media Software with Manifold Learning

Yuan¹,

Pang²,

Wang³

2021

Preprint

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The prosperous development of cloud computing and machine learning as a service has led to the widespread use of media software to process confidential media data. This paper explores an adversary's ability to launch side channel analyses (SCA) against media software to reconstruct confidential media inputs. Recent advances in representation learning and perceptual learning inspired us to consider the reconstruction of media inputs from side channel traces as a cross-modality manifold learning task that can be addressed in a unified manner with an autoencoder framework trained to learn the mapping between media inputs and side channel observations. We further enhance the autoencoder with attention to localize the program points that make the primary contribution to SCA, thus automatically pinpointing information-leakage points in media software. We also propose a novel and highly effective defensive technique called perception blinding that can perturb media inputs with perception masks and mitigate manifold learning-based SCA.Our evaluation exploits three popular media software to reconstruct inputs in image, audio, and text formats. We analyze three common side channels -cache bank, cache line, and page tables -and userspace-only cache set accesses logged by standard Prime+Probe. Our framework successfully reconstructs high-quality confidential inputs from the assessed media software and automatically pinpoint their vulnerable program points, many of which are unknown to the public. We further show that perception blinding can mitigate manifold learning-based SCA with negligible extra cost.

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“…Neither of these works looked at the problem of sampling. In a concurrent and independent work, Shi [34] has developed a data-oblivious sampling algorithm for generating one random sample from a stream of elements. In comparison, we consider the setting where k samples need to be generated from a dataset of n elements, thereby amortising the cost of processing the dataset across multiple samples.…”

Section: Related Workmentioning

confidence: 99%

Oblivious Sampling Algorithms for Private Data Analysis

Sasy,

Ohrimenko

2020

Preprint

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We study secure and privacy-preserving data analysis based on queries executed on samples from a dataset. Trusted execution environments (TEEs) can be used to protect the content of the data during query computation, while supporting differential-private (DP) queries in TEEs provides record privacy when query output is revealed. Support for sample-based queries is attractive due to privacy amplification since not all dataset is used to answer a query but only a small subset. However, extracting data samples with TEEs while proving strong DP guarantees is not trivial as secrecy of sample indices has to be preserved. To this end, we design efficient secure variants of common sampling algorithms. Experimentally we show that accuracy of models trained with shuffling and sampling is the same for differentially private models for MNIST and CIFAR-10, while sampling provides stronger privacy guarantees than shuffling.

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Path Oblivious Heap: Optimal and Practical Oblivious Priority Queue

Cited by 16 publications

References 36 publications

Data Oblivious Algorithms for Multicores

Data Oblivious Algorithms for Multicores

Automated Side Channel Analysis of Media Software with Manifold Learning

Oblivious Sampling Algorithms for Private Data Analysis

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