In this paper, we show how our secured multiparty computation (SMC) protocols protect the data of an organization during the war from the cyberspace war when a large number of defense units interact with one another, while hiding the identity and computations done by them. SMC is a problem of information security when large organizations interact with one another for huge data sharing and data exchange. It is quite possible that during sharing and exchange, the private data also get hacked. In order to protect and secure the private data, the protocols of SMC need to be deployed in the large computer networks on which the organizations work. The protocols work at the micro-level in terms of cryptography with which the data are encrypted and then shared, while allowing the keys to be used for sharable data while also keeping the keys untouched for private data. At the macro level, multilevel architectures are used for different types of security to be achieved. The computation part of the secured multiparty computation is based on the algorithmic complexity theory. The algorithms realize the protocols in such a way that it is tedious to break (decrypt) the keys to hack the private data.
In this paper, we show how our secured multiparty computation (SMC) protocols protect the data of an organization during the war from the cyberspace war when a large number of defense units interact with one another, while hiding the identity and computations done by them. SMC is a problem of information security when large organizations interact with one another for huge data sharing and data exchange. It is quite possible that during sharing and exchange, the private data also get hacked. In order to protect and secure the private data, the protocols of SMC need to be deployed in the large computer networks on which the organizations work. The protocols work at the micro-level in terms of cryptography with which the data are encrypted and then shared, while allowing the keys to be used for sharable data while also keeping the keys untouched for private data. At the macro level, multilevel architectures are used for different types of security to be achieved. The computation part of the secured multiparty computation is based on the algorithmic complexity theory. The algorithms realize the protocols in such a way that it is tedious to break (decrypt) the keys to hack the private data.
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.
Secure Multi party Computation (SMC) is a paradigm that keeps data of individual parties as secret and provides the private data for computation to evaluate some function of their common interest. The outcome of the computation is made available to all the parties. Cloud Computing is a paradigm shift from the distributed computing where an organization uses resources "as service". With the"pay-as-you-go" model, an organization can improve efficiency while minimizing the expenditure and the operation overhead. However, users of the cloud are worrying about the privacy of their data that has been supplied to the cloud provider. The solutions to these problems can be provided by the protocols of the Secured Multi party Cloud Computing (SMCC). In this tutorial, we propose SMC solution techniques that can be embedded while designing architecture of cloud computing especially when multiple cloud users jointly compute some function of their private data inputs. With our experience of developing protocols and devising algorithms for SMC problem we anticipate a crucial role of SMC in cloud computing. In this tutorial, we explore such scenario while discussing various protocols of SMC and their applicability in cloud computing, with the design of SMCC Architecture.
Secure Multiparty Computation technique plays an important role in the security research of distributed systems.This paper firstly summarizes the advances in theoretical and practical studies on SMC,and then analysis the basic protocols and its related cryptographic algorithms.Secondly,the key tools,secret share,homomorphic public key cryptography,mix network,zero knowledge proof,oblivious transfer and private comparison protocol in implementation SMC are studied deeply.Finally,some research directions in this area are also proposed.
As a fundamental part of cryptography, secure multiparty computation (SMC) is a building block of various cryptographic protocols, and it is also a hot topic in the international cryptographic community.In recent years, many SMC problems, such as secret information comparison, secret set problems and secure multiparty computational geometry, have been widely studied.As many practical problems need to be described by rational numbers, it is both theoretically and practically important to study the SMC problems in the rational number field.However, most of the existing researches focus on integers and the studied data are mainly one-dimensional data.There are few researches on secure multiparty computation of multi-dimensional data in the rational number field, but they can’t be generalized.Based on the fractional representation of rational numbers, the new encoding schemes about rational numbers and rational number vectors were proposed, which could encode multi-dimensional data in the rational number field and provided new solutions for other SMC problems in the rational number field.Based on the encoding scheme and one-way hash function, some protocols were designed for equality problems and set problems in the rational number field.These protocols used basic arithmetic operation and hash operation to guarantee efficiency than existing related protocols.And these protocols didn’t limit the range of research data and they were more widely applicable.It proves that these protocols are secure in the semi-honest model using simulation paradigm, and demonstrates the efficiency and the applicability of these protocols by theoretical analysis and experiment.A practical example was also given to illustrate that approaches are more versatile, and they could also be directly used to solve some secure multiparty computational geometry problems in the rational number field.
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.
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.