Emerging smart contract systems over decentralized cryptocurrencies allow mutually distrustful parties to transact safely without trusted third parties. In the event of contractual breaches or aborts, the decentralized blockchain ensures that honest parties obtain commensurate compensation. Existing systems, however, lack transactional privacy. All transactions, including flow of money between pseudonyms and amount transacted, are exposed on the blockchain. We present Hawk, a decentralized smart contract system that does not store financial transactions in the clear on the blockchain, thus retaining transactional privacy from the public's view. A Hawk programmer can write a private smart contract in an intuitive manner without having to implement cryptography, and our compiler automatically generates an efficient cryptographic protocol where contractual parties interact with the blockchain, using cryptographic primitives such as zero-knowledge proofs. To formally define and reason about the security of our protocols, we are the first to formalize the blockchain model of cryptography. The formal modeling is of independent interest. We advocate the community to adopt such a formal model when designing applications atop decentralized blockchains.

https://www.the-blockchain.com/docs/Hawk%20-%20The%20Blockchain%20Model%20of%20Cryptography%20and%20Privacy-Preserving%20Smart%20Contracts.pdf

Hawk: The Blockchain Model of Cryptography and Privacy-Preserving Smart Contracts

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 introducing modern parallel programming paradigms to secure computation, enabling their secure execution on large datasets. To address this challenge, we present Graph SC, a framework that (i) provides a programming paradigm that allows non-cryptography experts to write secure code, (ii) brings parallelism to such secure implementations, and (iii) meets the need for obliviousness, thereby not leaking any private information. Using Graph SC, developers can efficiently implement an oblivious version of graph-based algorithms (including sophisticated data mining and machine learning algorithms) that execute in parallel with minimal communication overhead. Importantly, our secure version of graph-based algorithms incurs a small logarithmic overhead in comparison with the non-secure parallel version. We build Graph SC and demonstrate, using several algorithms as examples, that secure computation can be brought into the realm of practicality for big data analysis. Our secure matrix factorization implementation can process 1 million ratings in 13 hours, which is a multiple order-of-magnitude improvement over the only other existing attempt, which requires 3 hours to process 16K ratings.

/pdf/graphsc-parallel-secure-computation-made-easy-4y07dt2w6k.pdf

GraphSC: Parallel Secure Computation Made Easy

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-2uu5c8wznk.pdf

Oblivious Data Structures

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.

/pdf/scoram-oblivious-ram-for-secure-computation-26yw2nhz30.pdf

SCORAM: Oblivious RAM for Secure Computation

Edit distance has been proven to be an important and frequently-used metric in many human genomic research, with Similar Patient Query (SPQ) being a particularly promising and attractive example However, due to the widespread privacy concerns on revealing personal genomic data, the scope and scale of many novel use of genome edit distance are substantially limited While the problem of private genomic edit distance has been studied by the research community for over a decade [6], the state-of-the-art solution [31] is far from even close to be applicable to real genome sequences In this paper, we propose several private edit distance protocols that feature unprecedentedly high efficiency and precision Our construction is a combination of a novel genomic edit distance ap- proximation algorithm and new construction of private set difference size protocols With the private edit distance based secure SPQ primitive, we propose GENSETS, a genome-wide, privacy- preserving similar patient query system It is able to support search- ing large-scale, distributed genome databases across the nation We have implemented a prototype of GENSETS The experimental results show that, with 100 Mbps network connection, it would take GENSETS less than 200 minutes to search through 1 million breast cancer patients (distributed nation-wide in 250 hospitals, each having 4000 patients), based on edit distances between their genomes of lengths about 75 million nucleotides each

/pdf/efficient-genome-wide-privacy-preserving-similar-patient-1qjo1fkqgd.pdf

Xiao Shaun Wang

Papers

ObliVM: A Programming Framework for Secure Computation

GraphSC: Parallel Secure Computation Made Easy

Oblivious Data Structures

SCORAM: Oblivious RAM for Secure Computation

Efficient Genome-Wide, Privacy-Preserving Similar Patient Query based on Private Edit Distance