Biblio

Secure multiparty computation (SMC) is a promising technology for privacy-preserving collaborative computation. In the last years several feasibility studies have shown its practical applicability in different fields. However, it is recognized that administration, and management overhead of SMC solutions are still a problem. A vital next step is the incorporation of SMC in the emerging fields of the Internet of Things and (smart) dynamic environments. In these settings, the properties of these contexts make utilization of SMC even more challenging since some vital premises for its application regarding environmental stability and preliminary configuration are not initially fulfilled. We bridge this gap by providing FlexSMC, a management and orchestration framework for SMC which supports the discovery of nodes, supports a trust establishment between them and realizes robustness of SMC session by handling nodes failures and communication interruptions. The practical evaluation of FlexSMC shows that it enables the application of SMC in dynamic environments with reasonable performance penalties and computation durations allowing soft real-time and interactive use cases.

We present improved protocols for the conversion of secret-shared bit-vectors into secret-shared integers and vice versa, for the use as subroutines in secure multiparty computation (SMC) protocols and for protocols verifying the adherence of parties to prescribed SMC protocols. The protocols are primarily designed for three-party computation with honest majority. We evaluate our protocols as part of the Sharemind three-party protocol set and see a general reduction of verification overheads, thereby increasing the practicality of covertly or actively secure Sharemind protocols.

Collaborative smart services provide functionalities which exploit data collected from different sources to provide benefits to a community of users. Such data, however, might be privacy sensitive and their disclosure has to be avoided. In this paper, we present a distributed multi-tier framework intended for smart-environment management, based on usage control for policy evaluation and enforcement on devices belonging to different collaborating entities. The proposed framework exploits secure multi-party computation to evaluate policy conditions without disclosing actual value of evaluated attributes, to preserve privacy. As reference example, a smart-grid use case is presented.

Centralized systems in the Internet of Things—be it local middleware or cloud-based services—fail to fundamentally address privacy of the collected data. We propose an architecture featuring secure multiparty computation at its core in order to realize data processing systems which already incorporate support for privacy protection in the architecture.

Currently, when companies conduct risk analysis of own networks and systems, it is common to outsource risk analysis to third-party experts. At that time, the company passes the information used for risk analysis including confidential information such as network configuration to third-party expert. It raises the risk of leakage and abuse of confidential information. Therefore, a method of risk analysis by using secure computation without passing confidential information of company has been proposed. Although Liu's method have firstly achieved secure risk analysis method using multiparty computation and attack tree analysis, it has several problems to be practical. In this paper, improvement of secure risk analysis method is proposed. It can dynamically reduce compilation time, enhance scale of target network and system without increasing execution time. Experimental work is carried out by prototype implementation. As a result, we achieved improved performance in compile time and enhance scale of target with equivalent performance on execution time.

Cloud computing is becoming the main computing model in the future due to its advantages such as high resource utilization rate and save high cost of performance. The public environments is become necessary to secure their storage and transmission against possible attacks such as known-plain-text attack and semantic security. How to ensure the data security and the privacy preserving, however, becomes a huge obstacle to its development. The traditional way to solve Secure Multiparty Computation (SMC) problem is using Trusted Third Party (TTP), however, TTPs are particularly hard to achieve and compute complexity. To protect user's privacy data, the encrypted outsourcing data are generally stored and processed in cloud computing by applying homomorphic encryption. According to above situation, we propose Elliptic Curve Cryptography (ECC) based homomorphic encryption scheme for SMC problem that is dramatically reduced computation and communication cost. It shows that the scheme has advantages in energy consumption, communication consumption and privacy protection through the comparison experiment between ECC based homomorphic encryption and RSA&Paillier encryption algorithm. Further evidence, the scheme of homomorphic encryption scheme based on ECC is applied to the calculation of GPS data of the earthquake and prove it is proved that the scheme is feasible, excellent encryption effect and high security.

Large scale biomedical research projects involve analysis of huge amount of genomic data which is owned by different data owners. The collection and storing of genomic data is sometimes beyond the capability of a sole organization. Genomic data sharing is a feasible solution to overcome this problem. These scenarios can be generalized into the problem of aggregating data distributed among multiple databases and owned by different data owners. However, we should guarantee that an adversary cannot learn anything about the data or the individual contribution of each party towards the final output of the computation. In this paper, we propose a practical solution for secure sharing and computation of genomic data. We adopt the Paillier cryptosystem and the order preserving encryption to securely execute the count query and the ranked query. Experimental results demonstrate that the computation time is realistic enough to make our system adoptable in the real world.

Motivated by applications in cryptography, we introduce and study the problem of distribution design. The goal of distribution design is to find a joint distribution on \$n\$ random variables that satisfies a given set of constraints on the marginal distributions. Each constraint can either require that two sequences of variables be identically distributed or, alternatively, that the two sequences have disjoint supports. We present several positive and negative results on the existence and efficiency of solutions for a given set of constraints. Distribution design can be seen as a strict generalization of several well-studied problems in cryptography. These include secret sharing, garbling schemes, and non-interactive protocols for secure multiparty computation. We further motivate the problem and our results by demonstrating their usefulness towards realizing non-interactive protocols for ad-hoc secure multiparty computation, in which any subset of the parties may choose to participate and the identity of the participants should remain hidden to the extent possible.

Function Secret Sharing (FSS), introduced by Boyle et al. (Eurocrypt 2015), provides a way for additively secret-sharing a function from a given function family F. More concretely, an m-party FSS scheme splits a function f : \0, 1\n -textgreater G, for some abelian group G, into functions f1,...,fm, described by keys k1,...,km, such that f = f1 + ... + fm and every strict subset of the keys hides f. A Distributed Point Function (DPF) is a special case where F is the family of point functions, namely functions f\_\a,b\ that evaluate to b on the input a and to 0 on all other inputs. FSS schemes are useful for applications that involve privately reading from or writing to distributed databases while minimizing the amount of communication. These include different flavors of private information retrieval (PIR), as well as a recent application of DPF for large-scale anonymous messaging. We improve and extend previous results in several ways: * Simplified FSS constructions. We introduce a tensoring operation for FSS which is used to obtain a conceptually simpler derivation of previous constructions and present our new constructions. * Improved 2-party DPF. We reduce the key size of the PRG-based DPF scheme of Boyle et al. roughly by a factor of 4 and optimize its computational cost. The optimized DPF significantly improves the concrete costs of 2-server PIR and related primitives. * FSS for new function families. We present an efficient PRG-based 2-party FSS scheme for the family of decision trees, leaking only the topology of the tree and the internal node labels. We apply this towards FSS for multi-dimensional intervals. We also present a general technique for extending FSS schemes by increasing the number of parties. * Verifiable FSS. We present efficient protocols for verifying that keys (k*/1,...,k*/m ), obtained from a potentially malicious user, are consistent with some f in F. Such a verification may be critical for applications that involve private writing or voting by many users.

In this work we propose a model for conducting efficient and mutually beneficial information sharing between two competing entities, focusing specifically on software vulnerability sharing. We extend the two-stage game-theoretic model proposed by Khouzani et al. [18] for bug sharing, addressing two key features: we allow security information to be associated with different categories and severities, but also remove a large proportion of player homogeneity assumptions the previous work makes. We then analyse how these added degrees of realism affect the trading dynamics of the game. Secondly, we develop a new private set operation (PSO) protocol that enables the removal of the trusted mediation requirement. The PSO functionality allows for bilateral trading between the two entities up to a mutually agreed threshold on the value of information shared, keeping all other input information secret. The protocol scales linearly with set sizes and we give an implementation that establishes the practicality of the design for varying input parameters. The resulting model and protocol provide a framework for practical and secure information sharing between competing entities.