Implementing conjunction obfuscation under entropic ring LWE

We address the practicality challenges of secure program obfuscation by implementing, optimizing, and experimentally assessing an approach to securely obfuscate conjunction programs proposed in [1]. Conjunction programs evaluate functions $f (x_1, . . . , x_L) = \wedge_{i \in I}$ $y_i$, where $y_i$ is either $x_i$ or $\neg x_i$ and $I \subseteq [L]$, and can be used as classifiers. Our obfuscation approach satisfies distributional Virtual Black Box (VBB) security based on reasonable hardness assumptions, namely an entropic variant of the Ring Learning with Errors (Ring-LWE) assumption. Prior implementations of secure program obfuscation techniques support either trivial programs like point functions, or support the obfuscation of more general but less efficient branching programs to satisfy Indistinguishability Obfuscation (IO), a weaker security model. Further, the more general implemented techniques, rather than relying on standard assumptions, base their security on conjectures that have been shown to be theoretically vulnerable. Our work is the first implementation of non-trivial program obfuscation based on polynomial rings. Our contributions include multiple design and implementation advances resulting in reduced program size, obfuscation runtime, and evaluation runtime by many orders of magnitude. We implement our design in software and experimentally assess performance in a commercially available multi-core computing environment. Our implementation achieves runtimes of 6.7 hours to securely obfuscate a 64-bit conjunction program and 2.5 seconds to evaluate this program over an arbitrary input. We are also able to obfuscate a 32-bit conjunction program with 53 bits of security in 7 minutes and evaluate the obfuscated program in 43 milliseconds on a commodity desktop computer, which implies that 32-bit conjunction obfuscation is already practical. Our graph-induced (directed) encoding implementation runs up to 25 levels, which is higher than previously reported in the literature for this encoding. Our design and implementation advances are applicable to obfuscating more general compute-and-compare programs and can also be used for many cryptographic schemes based on lattice trapdoors

Implementing Token-Based Obfuscation under (Ring) LWE

Token-based obfuscation (TBO) is an interactive approach to cryptographic program obfuscation that was proposed by Goldwasser et al. (STOC 2013) as a potentially more practical alternative to conventional non-interactive security models, such as Virtual Black Box (VBB) and Indistinguishability Obfuscation. We introduce a query-revealing variant of TBO, and implement in PALISADE several optimized query-revealing TBO constructions based on (Ring) LWE covering a relatively broad spectrum of capabilities: linear functions, conjunctions, and branching programs. Our main focus is the obfuscation of general branching programs, which are asymptotically more efficient and expressive than permutation branching programs traditionally considered in program obfuscation studies. Our work implements read-once branching programs that are significantly more advanced than those implemented by Halevi et al. (ACM CCS 2017), and achieves program evaluation runtimes that are two orders of magnitude smaller. Our implementation introduces many algorithmic and code-level optimizations, as compared to the original theoretical construction proposed by Chen et al. (CRYPTO 2018). These include new trapdoor sampling algorithms for matrices of ring elements, extension of the original LWE construction to Ring LWE (with a hardness proof for non-uniform Ring LWE), asymptotically and practically faster token generation procedure, Residue Number System procedures for fast large integer arithmetic, and others. We also present efficient implementations for TBO of conjunction programs and linear functions, which significantly outperform prior implementations of these obfuscation capabilities, e.g., our conjunction obfuscation implementation is one order of magnitude faster than the VBB implementation by Cousins et al. (IEEE S&P 2018). We also provide an example where linear function TBO is used for classifying an ovarian cancer data set. All implementations done as part of this work are packaged in a TBO toolkit that is made publicly available

Obfuscated Fuzzy Hamming Distance and Conjunctions from Subset Product Problems

We consider the problem of obfuscating programs for fuzzy matching (in other words, testing whether the Hamming distance between an $n$-bit input and a fixed $n$-bit target vector is smaller than some predetermined threshold). This problem arises in biometric matching and other contexts. We present a virtual-black-box (VBB) secure and input-hiding obfuscator for fuzzy matching for Hamming distance, based on certain natural number-theoretic computational assumptions. In contrast to schemes based on coding theory, our obfuscator is based on computational hardness rather than information-theoretic hardness, and can be implemented for a much wider range of parameters. The Hamming distance obfuscator can also be applied to obfuscation of matching under the $\ell_1$ norm on $\mathbb{Z}^n$. We also consider obfuscating conjunctions. Conjunctions are equivalent to pattern matching with wildcards, which can be reduced in some cases to fuzzy matching. Our approach does not cover as general a range of parameters as other solutions, but it is much more compact. We study the relation between our obfuscation schemes and other obfuscators and give some advantages of our solution

New Techniques for Obfuscating Conjunctions

A conjunction is a function $f(x_1,\dots,x_n) = \bigwedge_{i \in S} l_i$ where $S \subseteq [n]$ and each $l_i$ is $x_i$ or $\neg x_i$. Bishop et al. (CRYPTO 2018) recently proposed obfuscating conjunctions by embedding them in the error positions of a noisy Reed-Solomon codeword and encoding the codeword in a group exponent. They prove distributional virtual black box (VBB) security in the generic group model for random conjunctions where $|S| \geq 0.226n$. While conjunction obfuscation was known from LWE due to Wichs and Zirdelis (FOCS 2017) and Goyal et al. (FOCS 2017), these constructions rely on substantial technical machinery. In this work, we conduct an extensive study of simple conjunction obfuscation techniques. - We abstract the Bishop et al. scheme to obtain an equivalent yet more efficient dual\u27\u27 scheme that can handle conjunctions over exponential size alphabets. This scheme admits a straightforward proof of generic group security, which we combine with a novel combinatorial argument to obtain distributional VBB security for $|S|$ of any size. - If we replace the Reed-Solomon code with a random binary linear code, we can prove security from standard LPN and avoid encoding in a group. This addresses an open problem posed by Bishop et al. to prove security of this simple approach in the standard model. - We give a new construction that achieves information theoretic distributional VBB security and weak functionality preservation for $|S| \geq n - n^\delta$ and $\delta < 1$. Assuming discrete log and $\delta < 1/2$, we satisfy a stronger notion of functionality preservation for computationally bounded adversaries while still achieving information theoretic security

CENSOR: Privacy-preserving Obfuscation for Outsourcing SAT formulas

We propose a novel obfuscation technique that can be used to outsource hard satisfiability (SAT) formulas to the cloud. Servers with large computational power are typically used to solve SAT instances that model real-life problems in task scheduling, AI planning, circuit verification and more. However, outsourcing data to the cloud may lead to privacy and information breaches since satisfying assignments may reveal considerable information about the underlying problem modeled by SAT. In this work, we develop CENSOR (privaCy prEserviNg obfuScation for Outsourcing foRmulas), a novel SAT obfuscation framework that resembles Indistinguishability Obfuscation. At the core of the framework lies a mechanism that transforms any formula to a random one with the same number of satisfying assignments. As a result, obfuscated formulas are indistinguishable from each other thus preserving the input-output privacy of the original SAT instance. Contrary to prior solutions that are rather adhoc in nature, we formally prove the security of our scheme. Additionally, we show that obfuscated formulas are within a polynomial factor of the original ones thus achieving polynomial slowdown. Finally, the whole process is efficient in practice, allowing solutions to original instances to be easily recovered from obfuscated ones. A byproduct of our method is that all NP problems can be potentially outsourced to the cloud by means of reducing to SAT

Building an Efficient Lattice Gadget Toolkit: Subgaussian Sampling and More

Many advanced lattice cryptography applications require efficient algorithms for inverting the so-called gadget matrices, which are used to formally describe a digit decomposition problem that produces an output with specific (statistical) properties. The common gadget inversion problems are the classical (often binary) digit decomposition, subgaussian decomposition, Learning with Errors (LWE) decoding, and discrete Gaussian sampling. In this work, we build and implement an efficient lattice gadget toolkit that provides a general treatment of gadget matrices and algorithms for their inversion/sampling. The main contribution of our work is a set of new gadget matrices and algorithms for efficient subgaussian sampling that have a number of major theoretical and practical advantages over previously known algorithms. Another contribution deals with efficient algorithms for LWE decoding and discrete Gaussian sampling in the Residue Number System (RNS) representation. We implement the gadget toolkit in PALISADE and evaluate the performance of our algorithms both in terms of runtime and noise growth. We illustrate the improvements due to our algorithms by implementing a concrete complex application, key-policy attribute-based encryption (KP-ABE), which was previously considered impractical for CPU systems (except for a very small number of attributes). Our runtime improvements for the main bottleneck operation based on subgaussian sampling range from 18x (for 2 attributes) to 289x (for 16 attributes; the maximum number supported by a previous implementation). Our results are applicable to a wide range of other advanced applications in lattice cryptography, such as GSW-based homomorphic encryption schemes, leveled fully homomorphic signatures, key-hiding PRFs and other forms of ABE, some program obfuscation constructions, and more

On Low-End Obfuscation and Learning

Most recent works on cryptographic obfuscation focus on the high-end regime of obfuscating general circuits while guaranteeing computational indistinguishability between functionally equivalent circuits. Motivated by the goals of simplicity and efficiency, we initiate a systematic study of "low-end" obfuscation, focusing on simpler representation models and information-theoretic notions of security. We obtain the following results. - Positive results via "white-box" learning. We present a general technique for obtaining perfect indistinguishability obfuscation from exact learning algorithms that are given restricted access to the representation of the input function. We demonstrate the usefulness of this approach by obtaining simple obfuscation for decision trees and multilinear read-k arithmetic formulas. - Negative results via PAC learning. A proper obfuscation scheme obfuscates programs from a class C by programs from the same class. Assuming the existence of one-way functions, we show that there is no proper indistinguishability obfuscation scheme for k-CNF formulas for any constant k ? 3; in fact, even obfuscating 3-CNF by k-CNF is impossible. This result applies even to computationally secure obfuscation, and makes an unexpected use of PAC learning in the context of negative results for obfuscation. - Separations. We study the relations between different information-theoretic notions of indistinguishability obfuscation, giving cryptographic evidence for separations between them

Pseudorandom Functions: Three Decades Later

In 1984, Goldreich, Goldwasser and Micali formalized the concept of pseudorandom functions and proposed a construction based on any length-doubling pseudorandom generator. Since then, pseudorandom functions have turned out to be an extremely influential abstraction, with applications ranging from message authentication to barriers in proving computational complexity lower bounds. In this tutorial we survey various incarnations of pseudorandom functions, giving self-contained proofs of key results from the literature. Our main focus is on feasibility results and constructions, as well as on limitations of (and induced by) pseudorandom functions. Along the way we point out some open questions that we believe to be within reach of current techniques

Gadgets and Gaussians in Lattice-Based Cryptography

This dissertation explores optimal algorithms employed in lattice-based cryptographic schemes. Chapter 2 focuses on optimizing discrete gaussian sampling on "gadget" and algebraic lattices. These gaussian sampling algorithms are used in lattice-cryptography's most efficient trapdoor mechanism for the SIS and LWE problems: "MP12" trapdoors. However, this trapdoor mechanism was previously not optimized and inefficient (or not proven to be statistically correct) for structured lattices (ring-SIS/LWE), lattice-cryptography's most efficient form, where the modulus is often a prime. The algorithms in this chapter achieve optimality in this regime and have (already) resulted in drastic efficiency improvement in independent implementations.Chapter 3 digs deeper into the gadget lattice's associated algorithms. Specifically, we explore efficiently sampling a simple subgaussian distribution on gadget lattices, and we optimize LWE decoding on gadget lattices. These subgaussian sampling algorithms correspond to a randomized bit-decomposition needed in lattice-based schemes with homomorphic properties like fully homomorphic encryption (FHE). Next, we introduce a general class of "Chinese Remainder Theorem" (CRT) gadgets. These gadgets allow advanced lattice-based schemes to avoid multi-precision arithmetic when the applications modulus is larger than 64 bits. The algorithms presented in the first two chapters improve the efficiency of many lattice-based cryptosystems: digital signature schemes, identity-based encryption schemes, as well as more advanced schemes like fully-homomorphic encryption and attribute-based encryption. In the final chapter, we take a closer look at the random matrices used in trapdoor lattices. First, we revisit the constants in the concentration bounds of subgaussian random matrices. Then, we provide experimental evidence for a simple heuristic regarding the singular values of matrices with entries drawn from commonly used distributions in cryptography. Though the proofs in this chapter are dense, cryptographers need a strong understanding of the singular values of these matrices since their maximum singular value determines the concrete security of the trapdoor scheme's underlying SIS problem