The Secure Multiparty computation is characterized by computation by a set of multiple parties each participating using the private input they have. There are different types of models for Secure Multiparty computation based on assumption about the type of adversaries each model is assumed to protect against including Malicious and Covert Adversaries. The model may also assume a trusted setup with either using a Public Key Infrastructure or a using a Common Reference String. Secure Multiparty Computation has a number of applications including Scientific Computation, Database Querying and Data Mining.
The Secure Multiparty computation is characterized by computation by a set of multiple parties each participating using the private input they have. There are different types of models for Secure Multiparty computation based on assumption about the type of adversaries each model is assumed to protect against including Malicious and Covert Adversaries. The model may also assume a trusted setup with either using a Public Key Infrastructure or a using a Common Reference String. Secure Multiparty Computation has a number of applications including Scientific Computation, Database Querying and Data Mining.
The goal of secure two-party computation is to enable two parties to cooperatively evaluate a function that takes private data from both parties as input without exposing any of the private data. At the end of the computation, the participants learn nothing more than the output of the function. The two-party secure computation systems have three properties: (1) the application involves inputs from two independent parties; (2) each party wants to keep its own data secret; and (3) the participants agree to reveal the output of the computation. That is, the result itself does not imply too much information about either party's private input. Informally, the security requirements are that nothing is learned from the protocol other than the output (privacy), and that the output is distributed according to the prescribed functionality (correctness). The threat models in the two-party computation assume the presence of three different types of adversaries: 1) Semi honest, 2) Malicious and 3) Covert.
Abstract. In the setting of secure multiparty computation, a set of parties wish to jointly compute some function of their inputs. Such a computation must preserve certain security properties, like privacy and correctness, even if some of the participating parties or an external adversary collude to attack the honest parties. Until this paper, all protocols for general secure computation assumed that the parties can communicate reliably via authenticated channels. In this paper, we consider the feasibility of secure computation without any setup assumption. We consider a completely unauthenticated setting, where all messages sent by the parties may be tampered with and modified by the adversary (without the honest parties being able to detect this fact). In this model, it is not possible to achieve the same level of security as in the authenticated-channel setting. Nevertheless, we show that meaningful security guarantees can be provided. In particular, we define a relaxed notion of what it means to “securely compute ” a function in the unauthenticated setting. Then, we construct protocols for securely realizing any functionality in the stand-alone model, with no setup assumptions whatsoever. In addition, we construct universally composable protocols for securely realizing any functionality in the common reference string model (while still in an unauthenticated network). We also show that our protocols can be used to provide conceptually simple and unified solutions to a number of problems that were studied separately in the past, including password-based authenticated key exchange and non-malleable commitments. 1
Multiparty computation protocols (MPC) are said to be secure against covert adversaries if the honest parties are guaranteed to detect any misbehavior by the malicious parties with a constant probability. Protocols that, upon detecting a cheating attempt, additionally allow the honest parties to compute certificates, which enable third parties to be convinced of the malicious behavior of the accused parties, are called publicly verifiable. In this work, we make several contributions to the domain of MPC with security against covert adversaries. We identify a subtle flaw in a protocol of Goyal, Mohassel, and Smith (Eurocrypt 2008), meaning that their protocol does not allow to identify a cheating party, and show how to fix their original construction to obtain security against covert adversaries. We present generic compilers that transform arbitrary passively secure preprocessing protocols, i.e. protocols where the parties have no private inputs, into protocols that are secure against covert adversaries and publicly verifiable. Using our compiler, we construct the first efficient variants of the BMR and the SPDZ protocols that are secure and publicly verifiable against a covert adversary that corrupts all but one party, and also construct variants with covert security and identifiable abort. We observe that an existing impossibility result by Ishai, Ostrovsky, and Seyalioglu (TCC 2012) can be used to show that there exist certain functionalities that cannot be realized by parties, that have oracle-access to broadcast and arbitrary two-party functionalities, with information-theoretic security against a covert adversary.
Many services that people use daily require computation that depends on the private data of multiple parties. While the utility of the final result of such interactions outweighs the privacy concerns related to output release, the inputs for such computations are much more sensitive and need to be protected. Secure multiparty computation (MPC) considers the question of constructing computation protocols that reveal nothing more about their inputs than what is inherently leaked by the output. There have been strong theoretical results that demonstrate that every functionality can be computed securely. However, these protocols remain unused in practical solutions since they introduce efficiency overhead prohibitive for most applications. Generic multiparty computation techniques address homogeneous setups with respect to the resources available to the participants and the adversarial model. On the other hand, realistic scenarios present a wide diversity of heterogeneous environments where different participants have different available resources and different incentives to misbehave and collude. In this thesis we introduce techniques for multiparty computation that focus on heterogeneous settings. We present solutions tailored to address different types of asymmetric constraints and improve the efficiency of existing approaches in these scenarios. We tackle the question from three main directions: New Computational Models for MPC - We explore different computational models that enable us to overcome inherent inefficiencies of generic MPC solutions using circuit representation for the evaluated functionality. First, we show how we can use random access machines to construct MPC protocols that add only polylogarithmic overhead to the running time of the insecure version of the underlying functionality. This allows to achieve MPC constructions with computational complexity sublinear in the size for their inputs, which is very important for computations that use large databases. We also consider multivariate polynomials which yield more succinct representations for the functionalities they implement than circuits, and at the same time a large collection of problems are naturally and efficiently expressed as multivariate polynomials. We construct an MPC protocol for multivariate polynomials, which improves the communication complexity of corresponding circuit solutions, and provides currently the most efficient solution for multiparty set intersection in the fully malicious case. Outsourcing Computation - The goal in this setting is to utilize the resources of a single powerful service provider for the work that computationally weak clients need to perform on their data. We present a new paradigm for constructing verifiable computation (VC) schemes, which enables a computationally limited client to verify efficiently the result of a large computation. Our construction is based on attribute-based encryption and avoids expensive primitives such as fully homomorphic encryption andprobabilistically checkable proofs underlying existing VC schemes. Additionally our solution enjoys two new useful properties: public delegation and verification. We further introduce the model of server-aided computation where we utilize the computational power of an outsourcing party to assist the execution and improve the efficiency of MPC protocols. For this purpose we define a new adversarial model of non-collusion, which provides room for more efficient constructions that rely almost completely only on symmetric key operations, and at the same time captures realistic settings for adversarial behavior. In this model we propose protocols for generic secure computation that offload the work of most of the parties to the computation server. We also construct a specialized server-aided two party set intersection protocol that achieves better efficiencies for the two participants than existing solutions. Outsourcing in many cases concerns only data storage and while outsourcing the data of a single party is useful, providing a way for data sharing among different clients of the service is the more interesting and useful setup. However, this scenario brings new challenges for access control since the access control rules and data accesses become private data for the clients with respect to the service provide. We propose an approach that offers trade-offs between the privacy provided for the clients and the communication overhead incurred for each data access. Efficient Private Search in Practice - We consider the question of private search from a different perspective compared to traditional settings for MPC. We start with strict efficiency requirements motivated by speeds of available hardware and what is considered acceptable overhead from practical point of view. Then we adopt relaxed definitions of privacy, which still provide meaningful security guarantees while allowing us to meet the efficiency requirements. In this setting we design a security architecture and implement a system for data sharing based on encrypted search, which achieves only 30% overhead compared to non-secure solutions on realistic workloads.
In this paper, comparative study of different available Secure Multiparty Computation (SMC) protocols have been addressed. In SMC, a set of parties wishes to jointly compute some function on their inputs. This computation must preserve certain security properties, like privacy and correctness. The general approach for such kind of computation is to make use of trusted third party to do the computation and then announce the result publicly. The major problem with this approach is that it is difficult to find a third party which is trusted by all the parties providing the inputs. This implies that the data of parties must be secured. Security is meant to achieve correctness of the result of computation and keeping the party’s input private even if some of the parties are corrupted.