Usually, a proof of a theorem contains more knowledge than the mere fact that the theorem is true. For instance, to prove that a graph is Hamiltonian it suffices to exhibit a Hamiltonian tour in it; however, this seems to contain more knowledge than the single bit Hamiltonian/non-Hamiltonian.In this paper a computational complexity theory of the “knowledge” contained in a proof is developed. Zero-knowledge proofs are defined as those proofs that convey no additional knowledge other than the correctness of the proposition in question. Examples of zero-knowledge proof systems are given for the languages of quadratic residuosity and 'quadratic nonresiduosity. These are the first examples of zero-knowledge proofs for languages not known to be efficiently recognizable.

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The knowledge complexity of interactive proof-systems

Machine learning is widely used in practice to produce predictive models for applications such as image processing, speech and text recognition. These models are more accurate when trained on large amount of data collected from different sources. However, the massive data collection raises privacy concerns. In this paper, we present new and efficient protocols for privacy preserving machine learning for linear regression, logistic regression and neural network training using the stochastic gradient descent method. Our protocols fall in the two-server model where data owners distribute their private data among two non-colluding servers who train various models on the joint data using secure two-party computation (2PC). We develop new techniques to support secure arithmetic operations on shared decimal numbers, and propose MPC-friendly alternatives to non-linear functions such as sigmoid and softmax that are superior to prior work. We implement our system in C++. Our experiments validate that our protocols are several orders of magnitude faster than the state of the art implementations for privacy preserving linear and logistic regressions, and scale to millions of data samples with thousands of features. We also implement the first privacy preserving system for training neural networks.

/pdf/secureml-a-system-for-scalable-privacy-preserving-machine-2jlvrwyxkm.pdf

SecureML: A System for Scalable Privacy-Preserving Machine Learning

/pdf/secureml-a-system-for-scalable-privacy-preserving-machine-3zt40tob0l.pdf

SecureML: A System for Scalable Privacy-Preserving Machine Learning.

Privacy-preserving multi-party machine learning allows multiple organizations to perform collaborative data analytics while guaranteeing the privacy of their individual datasets. Using trusted SGX-processors for this task yields high performance, but requires a careful selection, adaptation, and implementation of machine-learning algorithms to provably prevent the exploitation of any side channels induced by data-dependent access patterns.

We propose data-oblivious machine learning algorithms for support vector machines, matrix factorization, neural networks, decision trees, and k-means clustering. We show that our efficient implementation based on Intel Skylake processors scales up to large, realistic datasets, with overheads several orders of magnitude lower than with previous approaches based on advanced cryptographic multi-party computation schemes.

/pdf/oblivious-multi-party-machine-learning-on-trusted-processors-55gbl3j38q.pdf

Oblivious multi-party machine learning on trusted processors

We design and develop Obli VM, a programming framework for secure computation. ObliVM offers a domain specific 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.

/pdf/oblivm-a-programming-framework-for-secure-computation-4b9daz728t.pdf

ObliVM: A Programming Framework for Secure Computation

We propose a new tree-based ORAM scheme called Circuit ORAM. Circuit ORAM makes both theoretical and practical contributions. From a theoretical perspective, Circuit ORAM shows that the well-known Goldreich-Ostrovsky logarithmic ORAM lower bound is tight under certain parameter ranges, for several performance metrics. Therefore, we are the first to give an answer to a theoretical challenge that remained open for the past twenty-seven years. Second, Circuit ORAM earns its name because it achieves (almost) optimal circuit size both in theory and in practice for realistic choices of block sizes. We demonstrate compelling practical perfor- mance and show that Circuit ORAM is an ideal candidate for secure multi-party computation applications.

/pdf/circuit-oram-on-tightness-of-the-goldreich-ostrovsky-lower-88pd473ckt.pdf

Circuit ORAM: On Tightness of the Goldreich-Ostrovsky Lower Bound.

We propose a new tree-based ORAM scheme called Circuit ORAM. Circuit ORAM makes both theoretical and practical contributions. From a theoretical perspective, Circuit ORAM shows that the well-known Goldreich-Ostrovsky logarithmic ORAM lower bound is tight under certain parameter ranges, for several performance metrics. Therefore, we are the first to give an answer to a theoretical challenge that remained open for the past twenty-seven years. Second, Circuit ORAM earns its name because it achieves (almost) optimal circuit size both in theory and in practice for realistic choices of block sizes. We demonstrate compelling practical performance and show that Circuit ORAM is an ideal candidate for secure multi-party computation applications.

https://dl.acm.org/doi/pdf/10.1145/2810103.2813634

Circuit ORAM: On Tightness of the Goldreich-Ostrovsky Lower Bound

We propose a simple and efficient framework for obtaining efficient constant-round protocols for maliciously secure two-party computation. Our framework uses a function-independent preprocessing phase to generate authenticated information for the two parties; this information is then used to construct a single "authenticated" garbled circuit which is transmitted and evaluated. We also show how to efficiently instantiate the preprocessing phase with a new, highly optimized version of the TinyOT protocol by Nielsen et al. Our protocol outperforms existing work in both the single-execution and amortized settings, with or without preprocessing: In the single-execution setting, our protocol evaluates an AES circuit with malicious security in 37 ms with an online time of 1 ms. Previous work with the best overall time requires 62 ms (with 14 ms online time); previous work with the best online time (also 1 ms) requires 124 ms overall.If we amortize over 1024 executions, each AES computation requires just 6.7 ms with roughly the same online time as above. The best previous work in the amortized setting has roughly the same total time but does not support function-independent preprocessing. Our work shows that the performance penalty for maliciously secure two-party computation (as compared to semi-honest security) is much smaller than previously believed.

https://dl.acm.org/doi/pdf/10.1145/3133956.3134053

Authenticated Garbling and Efficient Maliciously Secure Two-Party Computation

We propose a new, constant-round protocol for multi-party computation of boolean circuits that is secure against an arbitrary number of malicious corruptions. At a high level, we extend and generalize recent work of Wang et al. in the two-party setting. Namely, we design an efficient preprocessing phase that allows the parties to generate authenticated information; we then show how to use this information to distributively construct a single "authenticated" garbled circuit that is evaluated by one party. Our resulting protocol improves upon the state-of-the-art both asymptotically and concretely. We validate these claims via several experiments demonstrating both the efficiency and scalability of our protocol: Efficiency: For three-party computation over a LAN, our protocol requires only 95 ms to evaluate AES. This is roughly a 700X improvement over the best prior work, and only 2.5X slower than the best known result in the two-party setting. In general, for n-party computation our protocol improves upon prior work (which was never implemented) by a factor of more than 230n, e.g., an improvement of 3 orders of magnitude for 5-party computation. Scalability: We successfully executed our protocol with a large number of parties located all over the world, computing (for example) AES with 128 parties across 5 continents in under 3 minutes. Our work represents the largest-scale demonstration of secure computation to date.

https://dl.acm.org/doi/pdf/10.1145/3133956.3133979

Global-Scale Secure Multiparty Computation

We design novel, asymptotically more efficient data structures and algorithms for programs whose data access patterns exhibit some degree of predictability. To this end, we propose two novel techniques, a pointer-based technique and a locality-based technique. We show that these two techniques are powerful building blocks in making data structures and algorithms oblivious. Specifically, we apply these techniques to a broad range of commonly used data structures, including maps, sets, priority-queues, stacks, deques; and algorithms, including a memory allocator algorithm, max-flow on graphs with low doubling dimension, and shortest-path distance queries on weighted planar graphs. Our oblivious counterparts of the above outperform the best known ORAM scheme both asymptotically and in practice.

/pdf/oblivious-data-structures-5946vavfhq.pdf

Xiao Wang

Papers

Circuit ORAM: On Tightness of the Goldreich-Ostrovsky Lower Bound.

Circuit ORAM: On Tightness of the Goldreich-Ostrovsky Lower Bound

Authenticated Garbling and Efficient Maliciously Secure Two-Party Computation

Global-Scale Secure Multiparty Computation

Oblivious Data Structures.