Exponential Correlated Randomness Is Necessary in Communication-Optimal Perfectly Secure Two-Party Computation

Secure two-party computation is a cryptographic technique that enables two parties to compute a function jointly while keeping each input secret. It is known that most functions cannot be realized by information-theoretically secure two-party computation, but any function can be realized in the correlated randomness (CR) model, where a trusted dealer distributes input-independent CR to the parties beforehand. In the CR model, three kinds of complexities are mainly considered; the size of CR, the number of rounds, and the communication complexity. Ishai et al. (TCC 2013) showed that any function can be securely computed with optimal online communication cost, i.e., the number of rounds is one round and the communication complexity is the same as the input length, at the price of exponentially large CR. In this paper, we prove that exponentially large CR is necessary to achieve perfect security and online optimality for a general function and that the protocol by Ishai et al. is asymptotically optimal in terms of the size of CR. Furthermore, we also prove that exponentially large CR is still necessary even when we allow multiple rounds while keeping the optimality of communication complexity

SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning

Performing machine learning (ML) computation on private data while maintaining data privacy, aka Privacy-preserving Machine Learning~(PPML), is an emergent field of research. Recently, PPML has seen a visible shift towards the adoption of the Secure Outsourced Computation~(SOC) paradigm due to the heavy computation that it entails. In the SOC paradigm, computation is outsourced to a set of powerful and specially equipped servers that provide service on a pay-per-use basis. In this work, we propose SWIFT, a robust PPML framework for a range of ML algorithms in SOC setting, that guarantees output delivery to the users irrespective of any adversarial behaviour. Robustness, a highly desirable feature, evokes user participation without the fear of denial of service. At the heart of our framework lies a highly-efficient, maliciously-secure, three-party computation (3PC) over rings that provides guaranteed output delivery (GOD) in the honest-majority setting. To the best of our knowledge, SWIFT is the first robust and efficient PPML framework in the 3PC setting. SWIFT is as fast as (and is strictly better in some cases than) the best-known 3PC framework BLAZE (Patra et al. NDSS'20), which only achieves fairness. We extend our 3PC framework for four parties (4PC). In this regime, SWIFT is as fast as the best known fair 4PC framework Trident (Chaudhari et al. NDSS'20) and twice faster than the best-known robust 4PC framework FLASH (Byali et al. PETS'20). We demonstrate our framework's practical relevance by benchmarking popular ML algorithms such as Logistic Regression and deep Neural Networks such as VGG16 and LeNet, both over a 64-bit ring in a WAN setting. For deep NN, our results testify to our claims that we provide improved security guarantee while incurring no additional overhead for 3PC and obtaining 2x improvement for 4PC.Comment: This article is the full and extended version of an article to appear in USENIX Security 202

On the Power of Amortization in Secret Sharing: dd-Uniform Secret Sharing and CDS with Constant Information Rate

Consider the following secret-sharing problem. Your goal is to distribute a long file $s$ between $n$ servers such that $(d-1)$-subsets cannot recover the file, $(d+1)$-subsets can recover the file, and $d$-subsets should be able to recover $s$ if and only if they appear in some predefined list $L$. How small can the information ratio (i.e., the number of bits stored on a server per each bit of the secret) be? We initiate the study of such $d$-uniform access structures, and view them as a useful scaled-down version of general access structures. Our main result shows that, for constant $d$, any $d$-uniform access structure admits a secret sharing scheme with a *constant* asymptotic information ratio of $c_d$ that does not grow with the number of servers $n$. This result is based on a new construction of $d$-party Conditional Disclosure of Secrets (Gertner et al., JCSS \u2700) for arbitrary predicates over $n$-size domain in which each party communicates at most four bits per secret bit. In both settings, previous results achieved non-constant information ratio which grows asymptotically with $n$ even for the simpler (and widely studied) special case of $d=2$. Moreover, our results provide a unique example for a natural class of access structures $F$ that can be realized with information rate smaller than its bit-representation length $\log |F|$ (i.e., $\Omega( d \log n)$ for $d$-uniform access structures) showing that amortization can beat the representation size barrier. Our main result applies to exponentially long secrets, and so it should be mainly viewed as a barrier against amortizable lower-bound techniques. We also show that in some natural simple cases (e.g., low-degree predicates), amortization kicks in even for quasi-polynomially long secrets. Finally, we prove some limited lower-bounds, point out some limitations of existing lower-bound techniques, and describe some applications to the setting of private simultaneous messages

Instantaneous Decentralized Poker

We present efficient protocols for amortized secure multiparty computation with penalties and secure cash distribution, of which poker is a prime example. Our protocols have an initial phase where the parties interact with a cryptocurrency network, that then enables them to interact only among themselves over the course of playing many poker games in which money changes hands. The high efficiency of our protocols is achieved by harnessing the power of stateful contracts. Compared to the limited expressive power of Bitcoin scripts, stateful contracts enable richer forms of interaction between standard secure computation and a cryptocurrency. We formalize the stateful contract model and the security notions that our protocols accomplish, and provide proofs using the simulation paradigm. Moreover, we provide a reference implementation in Ethereum/Solidity for the stateful contracts that our protocols are based on. We also adopt our off-chain cash distribution protocols to the special case of stateful duplex micropayment channels, which are of independent interest. In comparison to Bitcoin based payment channels, our duplex channel implementation is more efficient and has additional features

Tight Bounds on the Randomness Complexity of Secure Multiparty Computation

We revisit the question of minimizing the randomness complexity of protocols for secure multiparty computation (MPC) in the setting of perfect information-theoretic security. Kushilevitz and Mansour (SIAM J. Discret. Math., 1997) studied the case of $n$-party semi-honest MPC for the XOR function with security threshold $t<n$, showing that $O(t^2\log(n/t))$ random bits are sufficient and $\Omega(t)$ random bits are necessary. Their positive result was obtained via a non-explicit protocol, whose existence was proved using the probabilistic method. We essentially close the question by proving an $\Omega(t^2)$ lower bound on the randomness complexity of XOR, matching the previous upper bound up to a logarithmic factor (or constant factor when $t=\Omega(n)$). We also obtain an explicit protocol that uses $O(t^2\cdot\log^2n)$ random bits, matching our lower bound up to a polylogarithmic factor. We extend these results from XOR to general symmetric Boolean functions and to addition over a finite Abelian group, showing how to amortize the randomness complexity over multiple additions. Finally, combining our techniques with recent randomness-efficient constructions of private circuits, we obtain an explicit protocol for evaluating a general circuit $C$ using only $O(t^2\cdot\log |C|)$ random bits, by employing additional ``helper parties\u27\u27 who do not contribute any inputs. This upper bound too matches our lower bound up to a logarithmic factor

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

Cryptography from Anonymity

There is a vast body of work on {\em implementing} anonymous communication. In this paper, we study the possibility of using anonymous communication as a {\em building block}, and show that one can leverage on anonymity in a variety of cryptographic contexts. Our results go in two directions. \begin{itemize} \item{\bf Feasibility.} We show that anonymous communication over {\em insecure} channels can be used to implement unconditionally secure point-to-point channels, and hence general multi-party protocols with unconditional security in the presence of an honest majority. In contrast, anonymity cannot be generally used to obtain unconditional security when there is no honest majority. \item{\bf Efficiency.} We show that anonymous channels can yield substantial efficiency improvements for several natural secure computation tasks. In particular, we present the first solution to the problem of private information retrieval (PIR) which can handle multiple users while being close to optimal with respect to {\em both} communication and computation. A key observation that underlies these results is that {\em local randomization} of inputs, via secret-sharing, when combined with the {\em global mixing} of the shares, provided by anonymity, allows to carry out useful computations on the inputs while keeping the inputs private. \end{itemize

Random Sources in Private Computation

We consider multi-party information-theoretic private computation. Such computation inherently requires the use of local randomness by the parties, and the question of minimizing the total number of random bits used for given private computations has received considerable attention in the literature. In this work we are interested in another question: given a private computation, we ask how many of the players need to have access to a random source, and how many of them can be deterministic parties. We are further interested in the possible interplay between the number of random sources in the system and the total number of random bits necessary for the computation. We give a number of results. We first show that, perhaps surprisingly, $t$ players (rather than $t+1$) with access to a random source are sufficient for the information-theoretic $t$-private computation of any deterministic functionality over $n$ players for any $t<n/2$; by a result of (Kushilevitz and Mansour, PODC\u2796), this is best possible. This means that, counter intuitively, while private computation is impossible without randomness, it is possible to have a private computation even when the adversary can control all parties who can toss coins (and therefore sees all random coins). For randomized functionalities we show that $t+1$ random sources are necessary (and sufficient). We then turn to the question of the possible interplay between the number of random sources and the necessary number of random bits. Since for only very few settings in private computation meaningful bounds on the number of necessary random bits are known, we consider the AND function, for which some such bounds are known. We give a new protocol to $1$-privately compute the $n$-player AND function, which uses a single random source and $6$ random bits tossed by that source. This improves, upon the currently best known results (Kushilevitz et al., TCC\u2719), at the same time the number of sources and the number of random bits (KOPRT19 gives a $2$-source, $8$-bits protocol). This result gives maybe some evidence that for $1$-privacy, using the minimum necessary number of sources one can also achieve the necessary minimum number of random bits. We believe however that our protocol is of independent interest for the study of randomness in private computation

An End-to-End System for Large Scale P2P MPC-as-a-Service and Low-Bandwidth MPC for Weak Participants

Protocols for secure multiparty computation enable a set of parties to compute a joint function of their inputs, while preserving \emph{privacy}, \emph{correctness} and more. In theory, secure computation has broad applicability and can be used to solve many of the modern concerns around utilization of data and privacy. Huge steps have been made towards this vision in the past few years, and we now have protocols that can carry out large computations extremely efficiently, especially in the setting of an honest majority. However, in practice, there are still major barriers to widely deploying secure computation, especially in a decentralized manner. In this paper, we present the first end-to-end automated system for deploying large-scale MPC protocols between end users, called MPSaaS (for \textit{MPC system-as-a-service}). Our system enables parties to pre-enroll in an upcoming MPC computation, and then participate by either running software on a VM instance (e.g., in Amazon), or by running the protocol on a mobile app, in Javascript in their browser, or even on an IoT device. Our system includes an automation system for deploying MPC protocols, an administration component for setting up an MPC computation and inviting participants, and an end-user component for running the MPC protocol in realistic end-user environments. We demonstrate our system for a specific application of running secure polls and surveys, where the secure computation is run end-to-end with each party actually running the protocol (i.e., without relying on a set of servers to run the protocol for them). This is the first such system constructed, and is a big step forward to the goal of commoditizing MPC. One of the cryptographic difficulties that arise in this type of setting is due to the fact that end users may have low bandwidth connections, making it a challenge to run an MPC protocol with high bandwidth. We therefore present a protocol based on Beerliova-Trubiniova and Hirt (TCC 2008) with many optimizations, that has very low concrete communication, and the lowest published for small fields. Our protocol is secure as long as less than a third of the parties are \textit{malicious}, and is well suited for computing both arithmetic and Boolean circuits. We call our protocol HyperMPC and show that it has impressive performance. In particular, 150 parties can compute statistics---mean, standard deviation and regression---on 4,000,000 inputs (with a circuit of size 16,000,000 gates of which 6,000,000 are multiplication) in five minutes, and 10 parties can compute the same circuit in 30 seconds. Although our end-to-end system can be used to run any MPC protocol (and we have incorporated numerous protocols already), we demonstrate it for our new protocol that is optimized for end-users without high bandwidth