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 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.
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.
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.
The growth of the Internet has triggered tremendous opportunities for cooperative computation, where people are jointly conducting computation tasks based on the private inputs they each supply. These computations could occur between mutually untrusted parties, or even between competitors. Today, to conduct such computations, one entity must usually know the inputs from all the participants; however if nobody can be trusted enough to know all the inputs, privacy will become a primary concern. This problem is referred to as Secure Multi-party Computation Problem (SMC) in the literature. Research in the SMC area has been focusing on only a limited set of specific SMC problems, while privacy concerned cooperative computations call for SMC studies in a variety of computation domains. About the specific SMC problems, have some result [6,7,8,9,11,12,13], and all the result are concern Two-party computations. Recently, the paper of China has [23~28].
Secure multi-party computation (SMC) allows multiple parties to jointly and securely compute a function while preserving the privacy of the involved parties. In this regard, homomorphic cryptosystems allow users to perform addition or multiplication operations on encrypted values without having to decrypt the input values. In this paper, we propose both a cryptographic and a non-cryptographic privacy-preserving protocols that allow one party to collaboratively compute a private polynomial function with at least two other parties using semantically secure cryptosystems. In this respect, the function to be evaluated is kept private to the party in question. In addition, we assume that there is no fully trusted party and that all parties are semi-honest. Moreover, we assume that there is no collusion between the different parties. We present the two protocols in question and report on the underlying performance through the presentation of our implementation results. The proposed protocols are relevant in many applications such as evidence sharing in multi-party forensic investigations, situation awareness through the secure sharing in distributed monitoring of plan execution, etc.
Secure multi-party computation (SMC) is an emerging topic which has been drawing growing attention during recent decades. There are many examples which show importance of SMC constructions in practice, such as privacy-preserving decision making and machine learning, auctions, private set intersection, and others. In this tutorial, we provide a comprehensive coverage of SMC techniques, starting from precise definitions and fundamental techniques. Consequently, a significant portion of the tutorial focuses on recent advances in general SMC constructions. We cover garbled circuit evaluation (GCE) and linear secret sharing (LSS) which are commonly used for secure two-party and multi-party computation, respectively. The coverage includes both standard adversarial models: semi-honest and malicious. For GCE, we start with the original Yao's garbled circuits construction [30] for semi-honest adversaries and consequently cover its recent optimizations such as the "free XOR,'' the garbled row reduction, the half-gates optimization, and the use of AES NI techniques. We follow with a discussion of techniques for making GCE resilient to malicious behavior, which includes the cut-and-choose approach and additional techniques to deter known attacks in the presence of malicious participants. In addition, we include the-state-of-the-art protocols for oblivious transfer (OT) and OT extension in the presence of semi-honest and malicious users. For LSS, we start from standard solutions for the semi-honest adversarial model including [5, 28] and consequently move to recent efficient constructions for semi-honest and malicious adversarial models. The coverage includes different types of corruption thresholds (with and without honest majority), which imply different guarantees with respect to abort.
Secure Multi-Party Computation (SMC) allows multiple parties to compute some function of their inputs without disclosing the actual inputs to one another. Secure sum computation is an easily understood example and the component of the various SMC solutions. Secure sum computation allows parties to compute the sum of their individual inputs without disclosing the inputs to one another. In this paper, we propose a modified version of our ck-Secure Sum protocol with more security when a group of the computing parties conspire to know the data of some party.