Multiparty computation is a computation between multiple players which want to compute a common function based on private input. It was first proposed over 20 years ago and has since matured into a well established science. The goal of this thesis has been to develop efficient protocols for different operations used in multiparty computation and to propose uses for multiparty computation in real world systems. This thesis therefore gives the reader an overview of multiparty computation from the simplest primitives to the current state of software frameworks for multiparty computation, and provides ideas for future applications. Included in this thesis is a proposed model of multiparty computation based on a model of communication complexity. This model provides a good foundation for the included papers and for measuring the efficiency of multiparty computation protocols. In addition to this model, a more practical approach is also included, which examines different secret sharing schemes and how they are used as building blocks for basic multiparty computation operations. This thesis identifies five basic multiparty computation operations: sharing, recombining, addition, multiplication and negation, and shows how these five operations can be used to create more complex operations. In particular two operations “less-than” and “bitwise decomposition” are examined in detail in the included papers. “less-than” performs the “<” operator on two secret shared values with a secret shared result and “bitwise decomposition” takes a secret shared value and transforms it into a vector of secret shared bitwise values. The overall goal of this thesis has been to create efficient methods for multiparty computation so that it might be used for practical applications in the future.
Multiparty computation is a computation between multiple players which want to compute a common function based on private input. It was first proposed over 20 years ago and has since matured into a well established science. The goal of this thesis has been to develop efficient protocols for different operations used in multiparty computation and to propose uses for multiparty computation in real world systems. This thesis therefore gives the reader an overview of multiparty computation from the simplest primitives to the current state of software frameworks for multiparty computation, and provides ideas for future applications. Included in this thesis is a proposed model of multiparty computation based on a model of communication complexity. This model provides a good foundation for the included papers and for measuring the efficiency of multiparty computation protocols. In addition to this model, a more practical approach is also included, which examines different secret sharing schemes and how they are used as building blocks for basic multiparty computation operations. This thesis identifies five basic multiparty computation operations: sharing, recombining, addition, multiplication and negation, and shows how these five operations can be used to create more complex operations. In particular two operations “less-than” and “bitwise decomposition” are examined in detail in the included papers. “less-than” performs the “<” operator on two secret shared values with a secret shared result and “bitwise decomposition” takes a secret shared value and transforms it into a vector of secret shared bitwise values. The overall goal of this thesis has been to create efficient methods for multiparty computation so that it might be used for practical applications in the future.
Secure multiparty computation (SMC) has gained tremendous importance with the growth of the Internet and e-commerce, where mutually untrusted parties need to jointly compute a function of their private inputs. However, SMC protocols usually have very high computational complexities, rendering them practically unusable. In this paper, we tackle the problem of comparing two input values in a secure distributed fashion. We propose efficient secure comparison protocols for both the homomorphic encryption and secret sharing schemes. We also give experimental results to show their practical relevance.
Secure multiparty computation (SMC) allows a set of parties to jointly compute a function on private inputs such that, they learn only the output of the function, and the correctness of the output is guaranteed even when a subset of the parties is controlled by an adversary. SMC allows data to be kept in an uncompromisable form and still be useful, and it also gives new meaning to data ownership, allowing data to be shared in a useful way while retaining its privacy. Thus, applications of SMC hold promise for addressing some of the security issues information-driven societies struggle with. In this thesis, we implement two SMC protocols. Our primary objective is to gain a solid understanding of the basic concepts related to SMC. We present a brief survey of the field, with focus on SMC based on secret sharing. In addition to the protocol im- plementations, we implement circuit randomization, a common technique for efficiency improvement. The implemented protocols are run on a simulator to securely evaluate some simple arithmetic functions, and the round complexities of the implemented protocols are compared. Finally, we attempt to extend the implementation to support more general computations.
We propose constant-round protocols for interval tests, equality tests, and comparisons where shared secret inputs are not given bitwise. In [9]. Damgard et al. presented a novel protocol called the bit-decomposition, which can convert a polynomial sharing of an element in prime field p into sharings of bits. Though, by using the bit-decomposition protocol, those protocols can be constructed with constant round complexities theoretically, it involves expensive computation, leading to relatively high round and communication complexities. In this paper, we construct more efficient protocols for those protocols without relying on the bit-decomposition protocol. In the interval test protocol, checking whether a shared secret exists in the known interval is reduced to checking whether a bitwise-shared random secret exists in the appropriate interval. In the comparison protocol, comparing two shared secrets is reduced to comparing the two secrets via p/2 indirectly where p is an odd prime for an underlying linear secret sharing scheme. In the equality test protocol, checking whether two shared secrets are equal is reduced to checking whether the difference of the two secrets is zero and furthermore checking whether the difference is a zero is reduced to checking quadratice residuosity of a random secret in a probabilistic way.
Multiparty Computation (hereafter MPC) is a technique for allowing a set ofparticipants to run a secure computation with the assurance that the output is (1) correct, and (2) that a malicious party did not learn anything about the inputs of the honest parties besides what it can glean from the output. Impor- tantly much of cryptography is based on the assumption that an honest party has a private and secret input, and if the adversary learns this input the security of the scheme is broken. Then naturally the techniques of MPC can be applied to create threshold protocols for these cryptographic schemes to ensure that the secret inputs are not leaked and moreover that no group of parties smaller than the threshold know the input at all. While we have known how to generically evaluate MPC for any function since the 80s the question of efficiency still drives progress to this day. This thesis looks at two approaches for making secure com- putation more practical to deploy: developing specialized protocols for appli- cations such as distributed key generation and signing with RSA and ECDSA; and improving the state of the art of maliciously secure generic boolean-circuit MPC. We first look at work that produced a distributed key generation protocol for RSA that utilizes a novel candidate sampling mechanism to reduce waste in primality testing. The second work builds a threshold scheme for symmetric cryptographic primitives such as AES and SHA where there is limited algebraic structure to exploit by efficiently converting a generic n-of-n MPC protocol into a threshold t-of-n MPC protocol. Future directions in this thesis are in combin- ing techniques from the prior works to create a blind signature scheme for the Elliptic Curve Digital Signature Algorithm (ECDSA) that requires both alge- braic operations over elliptic curves and additionally symmetric evaluations of the SHA2 hash function.--Author's abstract
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.
The aim of this paper is to present some of the recent progress in efficient secure multiparty computation (MPC). In MPC we have a set of parties owning a set of private inputs. The par ties want to compute a function of their inputs, but they do not trust each other, therefore they need a cryptographic protocol to perform the computation in a way that 1) the output is correct and 2) cheating parties will not be able to learn any information about the honest parties inputs. Even though this problem has been formulated and essentially solved almost 30 years ago, practical solutions that can be relevant for real-world applications have been discovered only in the last few years. We will present some of these advances, trying to explain to a non-specialized audience the significance of the several existing security notions.