Time-Lock Puzzles from Randomized Encodings

Time-lock puzzles are a mechanism for sending messages "to the future". A sender can quickly generate a puzzle with a solution s that remains hidden until a moderately large amount of time t has elapsed. The solution s should be hidden from any adversary that runs in time significantly less than t, including resourceful parallel adversaries with polynomially many processors. While the notion of time-lock puzzles has been around for 22 years, there has only been a single candidate proposed. Fifteen years ago, Rivest, Shamir and Wagner suggested a beautiful candidate time-lock puzzle based on the assumption that exponentiation modulo an RSA integer is an "inherently sequential" computation. We show that various flavors of randomized encodings give rise to time-lock puzzles of varying strengths, whose security can be shown assuming the mere existence of non-parallelizing languages, which are languages that require circuits of depth at least t to decide, in the worst-case. The existence of such languages is necessary for the existence of time-lock puzzles. We instantiate the construction with different randomized encodings from the literature, where increasingly better efficiency is obtained based on increasingly stronger cryptographic assumptions, ranging from one-way functions to indistinguishability obfuscation. We also observe that time-lock puzzles imply one-way functions, and thus the reliance on some cryptographic assumption is necessary. Finally, generalizing the above, we construct other types of puzzles such as proofs of work from randomized encodings and a suitable worst-case hardness assumption (that is necessary for such puzzles to exist)

Privacy Preserving Computation in Home Loans using the FRESCO Framework

Secure Multiparty Computation (SMC) is a subfield of cryptography that allows multiple parties to compute jointly on a function without revealing their inputs to others. The technology is able to solve potential privacy issues that arises when a trusted third party is involved, like a server. This paper aims to evaluate implementations of Secure Multiparty Computation and its viability for practical use. The paper also seeks to understand and state the challenges and concepts of Secure Multiparty Computation through the construction of a home loan calculation application. Encryption over MPC is done within 2 to 2.5 Seconds. Up to 10K addition operations, MPC system performs very well and most applications will be sufficient within 10K additions

Time-Lock Puzzles from Randomized Encodings

Time-lock puzzles, introduced by May, Rivest, Shamir and Wagner, is a mechanism for sending messages ``to the future\u27\u27. A sender can quickly generate a puzzle with a solution $s$ that remains hidden until a moderately large amount of time $t$ has elapsed. The solution $s$ should be hidden from any adversary that runs in time significantly less than $t$, including resourceful parallel adversaries with polynomially many processors. While the notion of time-lock puzzles has been around for 22 years, there has only been a *single* candidate proposed. Fifteen years ago, Rivest, Shamir and Wagner suggested a beautiful candidate time-lock puzzle based on the assumption that exponentiation modulo an RSA integer is an ``inherently sequential\u27\u27 computation. We show that various flavors of {\em randomized encodings} give rise to time-lock puzzles of varying strengths, whose security can be shown assuming *the existence* of non-parallelizing languages, which are languages that require circuits of depth at least $t$ to decide, in the worst-case. The existence of such languages is necessary for the existence of time-lock puzzles. We instantiate the construction with different randomized encodings from the literature, where increasingly better efficiency is obtained based on increasingly stronger cryptographic assumptions, ranging from one-way functions to indistinguishability obfuscation. We also observe that time-lock puzzles imply one-way functions, and thus the reliance on some cryptographic assumption is necessary. Finally, generalizing the above, we construct other types of puzzles such as *proofs of work* from randomized encodings and a suitable worst-case hardness assumption (that is necessary for such puzzles to exist)

Cross&Clean: Amortized Garbled Circuits with Constant Overhead

Garbled circuits (GC) are one of the main tools for secure two-party computation. One of the most promising techniques for efficiently achieving active-security in the context of GCs is the so called \emph{cut-and-choose} approach, which in the last few years has received many refinements in terms of the number of garbled circuits which need to be constructed, exchanged and evaluated. In this paper we ask a simple question, namely \emph{how many garbled circuits are needed to achieve active security?} and we propose a novel protocol which achieves active security while using only a constant number of garbled circuits per evaluation in the amortized setting

Gazelle: A Low Latency Framework for Secure Neural Network Inference

The growing popularity of cloud-based machine learning raises a natural question about the privacy guarantees that can be provided in such a setting. Our work tackles this problem in the context where a client wishes to classify private images using a convolutional neural network (CNN) trained by a server. Our goal is to build efficient protocols whereby the client can acquire the classification result without revealing their input to the server, while guaranteeing the privacy of the server's neural network. To this end, we design Gazelle, a scalable and low-latency system for secure neural network inference, using an intricate combination of homomorphic encryption and traditional two-party computation techniques (such as garbled circuits). Gazelle makes three contributions. First, we design the Gazelle homomorphic encryption library which provides fast algorithms for basic homomorphic operations such as SIMD (single instruction multiple data) addition, SIMD multiplication and ciphertext permutation. Second, we implement the Gazelle homomorphic linear algebra kernels which map neural network layers to optimized homomorphic matrix-vector multiplication and convolution routines. Third, we design optimized encryption switching protocols which seamlessly convert between homomorphic and garbled circuit encodings to enable implementation of complete neural network inference. We evaluate our protocols on benchmark neural networks trained on the MNIST and CIFAR-10 datasets and show that Gazelle outperforms the best existing systems such as MiniONN (ACM CCS 2017) by 20 times and Chameleon (Crypto Eprint 2017/1164) by 30 times in online runtime. Similarly when compared with fully homomorphic approaches like CryptoNets (ICML 2016) we demonstrate three orders of magnitude faster online run-time

Gazelle: A Low Latency Framework for Secure Neural Network Inference

The growing popularity of cloud-based machine learning raises a natural question about the privacy guarantees that can be provided in such a setting. Our work tackles this problem in the context where a client wishes to classify private images using a convolutional neural network (CNN) trained by a server. Our goal is to build efficient protocols whereby the client can acquire the classification result without revealing their input to the server, while guaranteeing the privacy of the server's neural network. To this end, we design Gazelle, a scalable and low-latency system for secure neural network inference, using an intricate combination of homomorphic encryption and traditional two-party computation techniques (such as garbled circuits). Gazelle makes three contributions. First, we design the Gazelle homomorphic encryption library which provides fast algorithms for basic homomorphic operations such as SIMD (single instruction multiple data) addition, SIMD multiplication and ciphertext permutation. Second, we implement the Gazelle homomorphic linear algebra kernels which map neural network layers to optimized homomorphic matrix-vector multiplication and convolution routines. Third, we design optimized encryption switching protocols which seamlessly convert between homomorphic and garbled circuit encodings to enable implementation of complete neural network inference. We evaluate our protocols on benchmark neural networks trained on the MNIST and CIFAR-10 datasets and show that Gazelle outperforms the best existing systems such as MiniONN (ACM CCS 2017) by 20 times and Chameleon (Crypto Eprint 2017/1164) by 30 times in online runtime. Similarly when compared with fully homomorphic approaches like CryptoNets (ICML 2016) we demonstrate three orders of magnitude faster online run-time

Garbling, Stacked and Staggered: Faster k-out-of-n Garbled Function Evaluation

Stacked Garbling (SGC) is a Garbled Circuit (GC) improvement that efficiently and securely evaluates programs with conditional branching. SGC reduces bandwidth consumption such that communication is proportional to the size of the single longest program execution path, rather than to the size of the entire program. Crucially, the parties expend increased computational effort compared to classic GC. Motivated by procuring a subset in a menu of computational services or tasks, we consider GC evaluation of k-out-of-n branches, whose indices are known (or eventually revealed) to the GC evaluator E. Our stack-and-stagger technique amortizes GC computation in this setting. We retain the communication advantage of SGC, while significantly improving computation and wall-clock time. Namely, each GC party garbles (or evaluates) the total of n branches, a significant improvement over the O(nk) garblings/evaluations needed by standard SGC. We present our construction as a garbling scheme. Our technique brings significant overall performance improvement in various settings, including those typically considered in the literature: e.g. on a 1Gbps LAN we evaluate 16-out-of-128 functions ~7.68x faster than standard stacked garbling

Practical Garbled RAM: GRAM with O(log⁡2n)O(\log^2 n) Overhead

Garbled RAM (GRAM) is a powerful technique introduced by Lu and Ostrovsky that equips Garbled Circuit (GC) with a sublinear cost RAM without adding rounds of interaction. While multiple GRAM constructions are known, none are suitable for practice, due to costs that have high constants and poor scaling. We present the first GRAM suitable for practice. For computational security parameter $\kappa$ and for a size-$n$ RAM that stores blocks of size $w = \Omega(\log^2 n)$ bits, our GRAM incurs amortized $O(w \cdot \log^2 n \cdot \kappa)$ communication and computation per access. We evaluate the concrete cost of our GRAM; our approach outperforms trivial linear-scan-based RAM for as few as $512$ $128$-bit elements

Fast and Secure Three-party Computation: The Garbled Circuit Approach

Many deployments of secure multi-party computation (MPC) in practice have used information-theoretic three-party protocols that tolerate a single, semi-honest corrupt party, since these protocols enjoy very high efficiency. We propose a new approach for secure three-party computation (3PC) that improves security while maintaining practical efficiency that is competitive with traditional information-theoretic protocols. Our protocol is based on garbled circuits and provides security against a single, malicious corrupt party. Unlike information-theoretic 3PC protocols, ours uses a constant number of rounds. Our protocol only uses inexpensive symmetric-key cryptography: hash functions, block ciphers, pseudorandom generators (in particular, no oblivious transfers) and has performance that is comparable to that of Yao\u27s (semi-honest) 2PC protocol. We demonstrate the practicality of our protocol with an implementation based on the JustGarble framework of Bellare et al. (S&P 2013). The implementation incorporates various optimizations including the most recent techniques for efficient circuit garbling. We perform experiments on several benchmarking circuits, in different setups. Our experiments confirm that, despite providing a more demanding security guarantee, our protocol has performance comparable to existing information-theoretic 3PC