In the setting of secure multiparty computation, a set of parties wish to compute a joint function of their inputs, while preserving properties like privacy, correctness, and independence of inputs. One security property that has typically not been considered in the past relates to the length or size of the parties inputs. This is despite the fact that in many cases the size of a party’s input can be confidential. The reason for this omission seems to have been the folklore belief that, as with encryption, it is impossible to carry out non-trivial secure computation while hiding the size of parties ’ inputs. However some recent results (e.g., Ishai and Paskin at TCC 2007, Ateniese, De Cristofaro and Tsudik at PKC 2011) showed that it is possible to hide the input size of one of the parties for some limited class of functions, including secure two-party set intersection. This suggests that the folklore belief may not be fully accurate. In this work, we initiate a theoretical study of input-size hiding secure computation, and focus on the two-party case. We present definitions for this task, and deal with the subtleties that arise in the setting where there is no a priori polynomial bound on the parties ’ input sizes. Our definitional study yields a multitude of classes of input-size hiding computation, depending
In the setting of secure multiparty computation, a set of parties wish to compute a joint function of their inputs, while preserving properties like privacy, correctness, and independence of inputs. One security property that has typically not been considered in the past relates to the length or size of the parties inputs. This is despite the fact that in many cases the size of a party’s input can be confidential. The reason for this omission seems to have been the folklore belief that, as with encryption, it is impossible to carry out non-trivial secure computation while hiding the size of parties ’ inputs. However some recent results (e.g., Ishai and Paskin at TCC 2007, Ateniese, De Cristofaro and Tsudik at PKC 2011) showed that it is possible to hide the input size of one of the parties for some limited class of functions, including secure two-party set intersection. This suggests that the folklore belief may not be fully accurate. In this work, we initiate a theoretical study of input-size hiding secure computation, and focus on the two-party case. We present definitions for this task, and deal with the subtleties that arise in the setting where there is no a priori polynomial bound on the parties ’ input sizes. Our definitional study yields a multitude of classes of input-size hiding computation, depending
We introduce and study the notion of non-interactive secure multiparty computation (NIMPC). An NIMPC protocol for a function f(x1,..., xn) is specified by a joint probability distribution R = (R1,..., Rn) and local encoding functions Enci(xi, Ri), 1 ≤ i ≤ n. Given correlated randomness (R1,..., Rn) ∈R R, each party Pi, using its input xi and its randomness Ri, computes the message mi = Enci(xi, Ri). The messagesm1,...,mn can be used to decode f(x1,..., xn). For a set T ⊆ [n], the protocol is said to be T-robust if revealing the messages (Enci(xi, Ri))i 6∈T together with the random-ness (Ri)i∈T gives the same information about (xi)i 6∈T as an oracle access to the function f restricted to these input values. Namely, a coalition T can learn no more than the restriction of f fixing the inputs of uncorrupted parties, which, in this non-interactive setting, one cannot hope to hide. For 0 ≤ t ≤ n, the protocol is t-robust if it is T-robust for every T of size at most t and it is fully robust if it is n-robust. A 0-robust NIMPC protocol for f coincides with a protocol in the private simultaneous messages model of Feige et al. (STOC 1994). In the setting of computational (indistinguishability-based) security, fully robust NIMPC is implied by multi-input functional encryption, a notion that was recently introduced by Goldwasser et al. (Euro-
Traditional approaches to generic secure computation begin by representing the function f being computed as a circuit. If f depends on each of its input bits, this implies a protocol with complexity at least linear in the input size. In fact, linear running time is inherent for non-trivial functions since each party must "touch" every bit of their input lest information about the other party's input be leaked. This seems to rule out many applications of secure computation (e.g., database search) in scenarios where inputs are huge.
Secure Multi-party Computation (MPC) is one of the foundational achievements of modern cryptog-raphy, allowing multiple, distrusting, parties to jointly compute a function of their inputs, while revealing nothing but the output of the function. Following the seminal works of Yao and Goldreich, Micali and Wigderson and Ben-Or, Goldwasser and Wigderson, the study of MPC has expanded to consider a wide variety of questions, including variants in the attack model, underlying assumptions, complexity and composability of the resulting protocols. One question that appears to have received very little attention, however, is that of MPC over an underlying communication network whose structure is, in itself, sensitive information. This question, in addition to being of pure theoretical interest, arises naturally in many contexts: designing privacy-preserving social-networks, private peer-to-peer computations, vehicle-to-vehicle networks and the “in-ternet of things ” are some of the examples. In this paper, we initiate the study of “topology-hiding computation ” in the computational setting. We give formal definitions in both simulation-based and indistinguishability-based flavors. We show that, even for fail-stop adversaries, there are some strong impossibility results. Despite this, we show that protocols for topology-hiding computation can be constructed in the semi-honest and fail-stop models, if we somewhat restrict the set of nodes the adversary may corrupt.
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.
Most of this book has considered two-party and multiparty computation with security with (unfair) abort. This security notion allows the adversary to force the honest parties to abort even depending on the outputs of corrupted parties. (Note, however, that the adversary is not allowed to bias the output of honest parties in any other manner.) This relaxation of security is necessary if one wishes to tolerate arbitrarily many corruptions. A further relaxation considers computational security, where the adversary is assumed to be computationally bounded, e.g., probabilistic polynomial time. In this setting, any circuit can be securely realized even when arbitrarily many parties are corrupted, as long as one is willing to sacrifice fairness and robustness.