Oblivious Data Structures

Wang, Xiao; Nayak, Kartik; Liu, Chang; Chan, T.-H. Hubert; Shi, Elaine; Stefanov, Emil; Huang, Yan

doi:10.1145/2660267.2660314

Cited by 150 publications

(198 citation statements)

References 51 publications

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“…For example, efficient oblivious algorithms have been demonstrated for graph algorithms [22], [23], machine learning algorithms [24], [25], and data structures [26]- [28]. The customized approach can outperform generic ORAM, but is extremely costly in terms of the amount of cryptographic expertise and time consumed.…”

Section: B Oblivm Overview and Contributionsmentioning

confidence: 99%

“…We now explain how to provide programming abstractions for a class of pointer-based oblivious data structures described by Wang et al [26]. Figure 2b gives an example, where an expert programmer provides library support for implementing a class of pointer-based data structures such that a non-specialist programmer can implement data structures which will be compiled to efficient oblivious algorithms that outperform generic ORAM.…”

Section: B Programming Abstractions For Data Structuresmentioning

confidence: 99%

“…Implementing oblivious data structure abstractions in ObliVM. We assume that the reader is familiar with the oblivious data structure algorithmic techniques described by Wang et al [26]. To support efficient data structure implementations, an expert programmer implements two important objects (see Figure 2b):…”

Section: B Programming Abstractions For Data Structuresmentioning

confidence: 99%

See 2 more Smart Citations

ObliVM: A Programming Framework for Secure Computation

Liu

Wang

Nayak

et al. 2015

2015 IEEE Symposium on Security and Privacy

Self Cite

233

170

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We design and develop ObliVM, a programming framework for secure computation. ObliVM offers a domainspecific language designed for compilation of programs into efficient oblivious representations suitable for secure computation. ObliVM offers a powerful, expressive programming language and user-friendly oblivious programming abstractions. We develop various showcase applications such as data mining, streaming algorithms, graph algorithms, genomic data analysis, and data structures, and demonstrate the scalability of ObliVM to bigger data sizes. We also show how ObliVM significantly reduces development effort while retaining competitive performance for a wide range of applications in comparison with hand-crafted solutions. We are in the process of open-sourcing ObliVM and our rich libraries to the community (www.oblivm.com), offering a reusable framework to implement and distribute new cryptographic algorithms.

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Section: B Oblivm Overview and Contributionsmentioning

confidence: 99%

Section: B Programming Abstractions For Data Structuresmentioning

confidence: 99%

Section: B Programming Abstractions For Data Structuresmentioning

confidence: 99%

See 1 more Smart Citation

ObliVM: A Programming Framework for Secure Computation

Liu

Wang

Nayak

et al. 2015

2015 IEEE Symposium on Security and Privacy

Self Cite

233

170

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show abstract

“…This approach, however, is inefficient and not practical even for small graphs since it requires storage that is quadratic in the number of nodes in the graph and a large number of costly oblivious operations. Recent work by [49] presents an oblivious data structure for computing shortest paths on planar graphs using ORAM. For a sparse planar graph with O(n) edges, their approach requires O(n 1.5 ) space complexity at the cost of O( √ n log n) online query time.…”

Section: Related Workmentioning

confidence: 99%

Grecs

Meng

Kamara

Nissim

et al. 2015

Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security

107

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We propose graph encryption schemes that efficiently support approximate shortest distance queries on large-scale encrypted graphs. Shortest distance queries are one of the most fundamental graph operations and have a wide range of applications. Using such graph encryption schemes, a client can outsource large-scale privacy-sensitive graphs to an untrusted server without losing the ability to query it. Other applications include encrypted graph databases and controlled disclosure systems. We propose GRECS (stands for GRaph EnCryption for approximate Shortest distance queries) which includes three oracle encryption schemes that are provably secure against any semi-honest server. Our first construction makes use of only symmetric-key operations, resulting in a computationally-efficient construction. Our second scheme makes use of somewhat-homomorphic encryption and is less computationally-efficient but achieves optimal communication complexity (i.e. uses a minimal amount of bandwidth). Finally, our third scheme is both computationally-efficient and achieves optimal communication complexity at the cost of a small amount of additional leakage. We implemented and evaluated the efficiency of our constructions experimentally. The experiments demonstrate that our schemes are efficient and can be applied to graphs that scale up to 1.6 million nodes and 11 million edges.

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“…It is significantly faster than all existing ORAM constructions in secure computation setting. Further, this new scheme can also be used to build efficient oblivious data structures [36] for secure computation. Our implementation of scoram will be available online at http://www.oblivm.com.…”

Section: Contributionmentioning

confidence: 99%

Scoram

Wang

Huang

Chan

et al. 2014

Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security

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117

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Oblivious RAMs (ORAMs) have traditionally been measured by their bandwidth overhead and client storage. We observe that when using ORAMs to build secure computation protocols for RAM programs, the size of the ORAM circuits is more relevant to the performance.We therefore embark on a study of the circuit-complexity of several recently proposed ORAM constructions. Our careful implementation and experiments show that asymptotic analysis is not indicative of the true performance of ORAM in secure computation protocols with practical data sizes.We then present scoram, a heuristic compact ORAM design optimized for secure computation protocols. Our new design is almost 10x smaller in circuit size and also faster than all other designs we have tested for realistic settings (i.e., memory sizes between 4MB and 2GB, constrained by 2 −80 failure probability). scoram makes it feasible to perform secure computations on gigabyte-sized data sets.

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Oblivious Data Structures

Cited by 150 publications

References 51 publications

ObliVM: A Programming Framework for Secure Computation

ObliVM: A Programming Framework for Secure Computation

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