As far as we know, the literature on secure computation from cut-and-choose has focused on achieving computational security against malicious adversaries. It is unclear whether the idea of cut-and-choose can be adapted to secure computation with information-theoretic security. In this work we explore the possibility of using cut-and-choose in information theoretic setting for secure three-party computation (3PC). Previous work on 3PC has mainly focus on the semi-honest case, and is motivated by the observation that real-word deployments of multi-party computation (MPC) seem to involve few parties. We propose a new protocol for information-theoretically secure 3PC tolerating one malicious party with cheating probability $2^{-s}$ using $s$ runs of circuit computation in the cut-and-choose paradigm. The computational cost of our protocol is essentially only a small constant worse than that of state-of-the-art 3PC protocols against a semi-honest corruption, while its communication round is greatly reduced compared to other maliciously secure 3PC protocols in information-theoretic setting.
The application and analysis of the Cut-and-Choose technique in protocols secure against quantum adversaries is not a straightforward transposition of the classical case, among other reasons due to the difficulty to use rewinding in the quantum realm. We introduce a Quantum Computation Cut-and-Choose (QC-CC) technique which is a generalisation of the classical Cut-and-Choose in order to build quantum protocols secure against quantum covert adversaries. Such adversaries can deviate arbitrarily provided that their deviation is not detected. As an application of the QC-CC we give a protocol for s…
Rational secure multi-party computation (RSMC) means two or more rational parties to complete a function on private inputs. In the process, the rational parties choose strategies to maximize utility, which will cause players to maliciously execute the protocol and undermine the fairness and correctness of the protocol. To solve this problem, we leverage game theory to propose the direction entropy-based solution. First, we utilize the direction vector of the direction entropy to examine the player's strategy uncertainty and quantify its strategy from different dimensions. Specifically, when pa…
Due to the great development of secure multi-party computation, many practical secure computation schemes have been proposed. As an example, different secure auction mechanisms have been widely studied, which can protect bid privacy while satisfying various economic properties. However, as far as we know, none of them solve the secure computation problems for multiple data providers (e.g., secure cloud resource auctions) in the malicious security model. In this paper, we use the techniques of cut-and-choose and garbled circuits to propose a general secure computation framework for multiple dat…
Mining the spatial and temporal correlation of wind farm output data is beneficial for enhancing the precision of ultra-short-term wind power prediction. However, if the wind farms are owned by separate entities, they may be reluctant to share their data directly due to privacy concerns as well as business management regulation policies. Although cryptographic approaches have been designed to protect privacy in the process of data sharing, it is still a challenging problem to encrypt the original data while extracting the nonlinear relationship among multiple wind farms in the machine learning…
Let $π$ be an efficient two-party protocol that given security parameter $κ$, both parties output single bits $X_κ$ and $Y_κ$, respectively. We are interested in how $(X_κ,Y_κ)$ "appears" to an efficient adversary that only views the transcript $T_κ$. We make the following contributions: $\bullet$ We develop new tools to argue about this loose notion and show (modulo some caveats) that for every such protocol $π$, there exists an efficient simulator such that the following holds: on input $T_κ$, the simulator outputs a pair $(X'_κ,Y'_κ)$ such that $(X'_κ,Y'_κ,T_κ)$ is (somewhat) computationall…
We propose an efficient framework for enabling secure multi-party numerical computations in a Peer-to-Peer network. This problem arises in a range of applications such as collaborative filtering, distributed computation of trust and reputation, monitoring and other tasks, where the computing nodes is expected to preserve the privacy of their inputs while performing a joint computation of a certain function. Although there is a rich literature in the field of distributed systems security concerning secure multi-party computation, in practice it is hard to deploy those methods in very large scal…
In an onion routing protocol, messages travel through several intermediaries before arriving at their destinations, they are wrapped in layers of encryption (hence they are called "onions"). The goal is to make it hard to establish who sent the message. It is a practical and widespread tool for creating anonymous channels. For the standard adversary models -- network, passive, and active -- we present practical and provably secure onion routing protocols. Akin to Tor, in our protocols each party independently chooses the routing paths for his onions. For security parameter $λ$, our differentia…
Finding out the differences and commonalities between the knowledge of two parties is an important task. Such a comparison becomes necessary, when one party wants to determine how much it is worth to acquire the knowledge of the second party, or similarly when two parties try to determine, whether a collaboration could be beneficial. When these two parties cannot trust each other (for example, due to them being competitors) performing such a comparison is challenging as neither of them would be willing to share any of their assets. This paper addresses this problem for knowledge graphs, withou…
Consider a network of k parties, each holding a long sequence of n entries (a database), with minimum vertex-cut greater than t. We show that any empirical statistic across the network of databases can be computed by each party with perfect privacy, against any set of t < k/2 passively colluding parties, such that the worst-case distortion and communication cost (in bits per database entry) both go to zero as n, the number of entries in the databases, goes to infinity. This is based on combining a striking dimensionality reduction result for random sampling with unconditionally secure multi-pa…
Protocols satisfying Local Differential Privacy (LDP) enable parties to collect aggregate information about a population while protecting each user's privacy, without relying on a trusted third party. LDP protocols (such as Google's RAPPOR) have been deployed in real-world scenarios. In these protocols, a user encodes his private information and perturbs the encoded value locally before sending it to an aggregator, who combines values that users contribute to infer statistics about the population. In this paper, we introduce a framework that generalizes several LDP protocols proposed in the li…
Oblivious Transfer (OT) is a fundamental cryptographic protocol that finds a number of applications, in particular, as an essential building block for two-party and multi-party computation. We construct a round-optimal (2 rounds) universally composable (UC) protocol for oblivious transfer secure against active adaptive adversaries from any OW-CPA secure public-key encryption scheme with certain properties in the random oracle model (ROM). In terms of computation, our protocol only requires the generation of a public/secret-key pair, two encryption operations and one decryption operation, apart…
