Obliv-C: A Language for Extensible Data-Oblivious Computation

Many techniques for secure or private execution depend on executing programs in a data-oblivious way, where the same instructions execute independent of the private inputs which are kept in encrypted form throughout the computation. Designers of such computations today must either put substantial effort into constructing a circuit representation of their algorithm, or use a high-level language and lose the opportunity to make important optimizations or experiment with protocol variations. We show how extensibility can be improved by judiciously exposing the nature of data-oblivious computation. We introduce a new language that allows application developers to program secure computations without being experts in cryptography, while enabling programmers to create abstractions such as oblivious RAM and width-limited integers, or even new protocols without needing to modify the compiler. This paper explains the key language features that safely enable such extensibility and describes the simple implementation approach we use to ensure security properties are preserved

Two Halves Make a Whole: Reducing Data Transfer in Garbled Circuits using Half Gates

The well-known classical constructions of garbled circuits use four ciphertexts per gate, although various methods have been proposed to reduce this cost. The best previously known methods for optimizing AND gates (two ciphertexts; Pinkas et al., ASIACRYPT 2009) and XOR gates (zero ciphertexts; Kolesnikov & Schneider, ICALP 2008) were incompatible, so most implementations used the best known method compatible with free-XOR gates (three ciphertexts; Kolesnikov & Schneider, ICALP 2008). In this work we show how to simultaneously garble AND gates using two ciphertexts and XOR gates using zero ciphertexts, resulting in smaller garbled circuits than any prior scheme. The main idea behind our construction is to break an AND gate into two half-gates --- AND gates for which one party knows one input. Each half-gate can be garbled with a single ciphertext, so our construction uses two ciphertexts for each AND gate while being compatible with free-XOR gates. The price for the reduction in size is that the evaluator must perform two cryptographic operations per AND gate, rather than one as in previous schemes. We experimentally demonstrate that our garbling scheme leads to an overall decrease in time (up to 25%), bandwidth (up to 33%), and energy use (up to 20%) over several benchmark applications. We also initiate a study of lower bounds for garbled gate size, and show that our construction is optimal for a large class of garbling schemes encompassing all known practical garbling techniques

Deterministic, Stash-Free Write-Only ORAM

Write-Only Oblivious RAM (WoORAM) protocols provide privacy by encrypting the contents of data and also hiding the pattern of write operations over that data. WoORAMs provide better privacy than plain encryption and better performance than more general ORAM schemes (which hide both writing and reading access patterns), and the write-oblivious setting has been applied to important applications of cloud storage synchronization and encrypted hidden volumes. In this paper, we introduce an entirely new technique for Write-Only ORAM, called DetWoORAM. Unlike previous solutions, DetWoORAM uses a deterministic, sequential writing pattern without the need for any "stashing" of blocks in local state when writes fail. Our protocol, while conceptually simple, provides substantial improvement over prior solutions, both asymptotically and experimentally. In particular, under typical settings the DetWoORAM writes only 2 blocks (sequentially) to backend memory for each block written to the device, which is optimal. We have implemented our solution using the BUSE (block device in user-space) module and tested DetWoORAM against both an encryption only baseline of dm-crypt and prior, randomized WoORAM solutions, measuring only a 3x-14x slowdown compared to an encryption-only baseline and around 6x-19x speedup compared to prior work

Geppetto: Versatile Verifiable Computation

Cloud computing sparked interest in Verifiable Computation protocols, which allow a weak client to securely outsource computations to remote parties. Recent work has dramatically reduced the client’s cost to verify the correctness of results, but the overhead to produce proofs largely remains impractical. Geppetto introduces complementary techniques for reducing prover overhead and increasing prover flexibility. With Multi-QAPs, Geppetto reduces the cost of sharing state between computations (e.g., for MapReduce) or within a single computation by up to two orders of magnitude. Via a careful instantiation of cryptographic primitives, Geppetto also brings down the cost of verifying outsourced cryptographic computations (e.g., verifiably computing on signed data); together with Geppetto’s notion of bounded proof bootstrapping, Geppetto improves on prior bootstrapped systems by five orders of magnitude, albeit at some cost in universality. Geppetto also supports qualitatively new properties like verifying the correct execution of proprietary (i.e., secret) algorithms. Finally, Geppetto’s use of energy-saving circuits brings the prover’s costs more in line with the program’s actual (rather than worst-case) execution time. Geppetto is implemented in a full-fledged, scalable compiler that consumes LLVM code generated from a variety of apps, as well as a large cryptographic library

Circuit Structures for Improving Efficiency of Security and Privacy Tools

Abstract—Several techniques in computer security, including generic protocols for secure computation and symbolic execution, depend on implementing algorithms in static circuits. Despite substantial improvements in recent years, tools built using these techniques remain too slow for most practical uses. They require transforming arbitrary programs into either Boolean logic circuits, constraint sets on Boolean variables, or other equivalent representations, and the costs of using these tools scale directly with the size of the input circuit. Hence, techniques for more efficient circuit constructions have benefits across these tools. We show efficient circuit constructions for various simple but commonly used data structures including stacks, queues, and associative maps. While current practice requires effectively copying the entire structure for each operation, our techniques take advantage of locality and batching to provide amortized costs that scale polylogarithmically in the size of the structure. We demonstrate how many common array usage patterns can be significantly improved with the help of these circuit structures. We report on experiments using our circuit structures for both generic secure computation using garbled circuits and automated test input generation using symbolic execution, and demonstrate order of magnitude improvements for both applications. I

Removing the Bottleneck for Practical 2PC

Secure Two Party Computation (2PC) has the potential to facilitate a wide range of real life applications where privacy of the computation and participants is critical. Nevertheless, this potential has not translated to widespread industry acceptance due to performance issues. Over the years a significant research effort has focused on optimising the performance of 2PC. The computation complexity has been continually improved and recently, following circuit optimisations and hardware support for cryptographic operations, evaluations of 2PC on a single host currently produce efficient results. Unfortunately, when evaluated on remote hosts, the performance remains prohibitive for practical purposes. The bottleneck is believed to be the bandwidth. In this work we explore the networking layer of 2PC implementations and show that the performance bottleneck is inherent in the usage of TCP sockets in implementations of 2PC schemes. Through experimental evaluations, we demonstrate that other transport protocols can significantly improve the performance of 2PC, making it suitable for practical applications

Revisiting square-root ORAM : efficient random access in multi-party computation

Hiding memory access patterns is required for secure computation, but remains prohibitively expensive for many interesting applications. Prior work has either developed custom algorithms that minimize the need for data-dependant memory access, or proposed the use of Oblivious RAM (ORAM) to provide a general-purpose solution. However, most ORAMs are designed for client-server scenarios, and provide only asymptotic benefits in secure computation. Even the best prior schemes show concrete benefits over naïve linear scan only for array sizes greater than 100. This immediately implies each ORAM access is 100 times slower than a single access at a known location. Even then, prior evaluations ignore the substantial initialization cost of existing schemes. We show how the classical square-root ORAM of Goldreich and Ostrovsky can be modified to overcome these problems, even though it is asymptotically worse than the best known schemes. Specifically, we show a design that has over 100x lower initialization cost, and provides benefits over linear scan for just 8 blocks of data. For all benchmark applications we tried, including Gale-Shapley stable matching and the scrypt key derivation function, our scheme outperforms alternate approaches across a wide range of parameters, often by several orders of magnitude

Privacy-preserving distributed linear regression on high-dimensional data

We propose privacy-preserving protocols for computing linear regression models, in the setting where the training dataset is vertically distributed among several parties. Our main contribution is a hybrid multi-party computation protocol that combines Yao’s garbled circuits with tailored protocols for computing inner products. Like many machine learning tasks, building a linear regression model involves solving a system of linear equations. We conduct a comprehensive evaluation and comparison of different techniques for securely performing this task, including a new Conjugate Gradient Descent (CGD) algorithm. This algorithm is suitable for secure computation because it uses an efficient fixed-point representation of real numbers while maintaining accuracy and convergence rates comparable to what can be obtained with a classical solution using floating point numbers. Our technique improves on Nikolaenko et al.’s method for privacy-preserving ridge regression (S&P 2013), and can be used as a building block in other analyses. We implement a complete system and demonstrate that our approach is highly scalable, solving data analysis problems with one million records and one hundred features in less than one hour of total running time