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International Conference on Intelligent Networking and Collaborative Systems (INCoS)

A Non-interactive Secure Outsourced Computation Scheme in Multi-party Cloud

Shu Guo, Haixia Xu

Published Sep 1, 2012• 2 citations
Publisher

Abstract

Cloud computing is becoming concerned both in academia and industry and one of the most important applications is outsourced computation. A computationally weak client out sources tasks to one or more workers in cloud, and this is called outsourced computation. This paper presents an outsourced protocol based on Kamara's scheme [19], and it allows the client to outsource their computation to more than one workers while keeping privacy and correctness. Kamara proposed security definitions, while regretfully, didn't show the description of the protocol. This paper presents a specific scheme in which the workers computes directly on shares that client send to them, so the workers don't have to interactive with each other. This protocol has these advantages: the protocol keeps privacy of the original input, the workers have no interactivity with each other during the execution, the protocol keeps the threshold unchanged.

DOI
10.1109/incos.2012.50

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Cloud computing is becoming concerned both in academia and industry and one of the most important applications is outsourced computation. A computationally weak client out sources tasks to one or more workers in cloud, and this is called outsourced computation. This paper presents an outsourced protocol based on Kamara's scheme [19], and it allows the client to outsource their computation to more than one workers while keeping privacy and correctness. Kamara proposed security definitions, while regretfully, didn't show the description of the protocol. This paper presents a specific scheme in which the workers computes directly on shares that client send to them, so the workers don't have to interactive with each other. This protocol has these advantages: the protocol keeps privacy of the original input, the workers have no interactivity with each other during the execution, the protocol keeps the threshold unchanged.
Outsourcing computation to cloud server has recently become popular in cloud computing. Cloud computing technologies enable clients with limited computational resources to outsource their massive computations to powerful cloud servers. Outsourcing computation has some new concerns, such as outsourced data and result privacy, verifiability and efficiency. Matrix multiplication is one of the most basic computational problems. In this paper, we are motivated to design a secure and efficient protocol for outsourcing the massive matrix multiplication computations to the cloud server. The existing works are mostly under amortized model that have an expensive phase as offline mode. Our proposed scheme does not have any expensive phase. So the client can outsource its matrices in online mode in which the efficiency of outsourcing will be increased. In our scheme, the client encrypts two matrices and sends them to a semi-honest cloud server. The cloud server computes matrix multiplication and a proof. After that, it sends the encrypted result to the client and finally the client checks the validity of the computation and decrypts the result. Our proposed scheme achieves privacy protection of outsourced data and multiplication result, unforgeability of proof, verification and high efficiency.
The rapid development of cloud computing has probably benefited each of us. However, the privacy risks brought by untrusty cloud servers arise the attention of more and more people and legislatures. In the last two decades, plenty of works seek the way of outsourcing various specific tasks while ensuring the security of private data. Although the addition and multiplication are enough for implementing any functions, the direct utilization of existing schemes like homomorphic encryption will lead to significant efficiency and accuracy loss, which is not suitable for outsourcing computation tasks. The tasks to be outsourced are endless, however, the involved calculations are similar. In this paper, inspired by additive secret sharing and multiplicative secret sharing technologies, we construct a series of novel protocols which support the common secure calculations on numbers (e.g., basic elementary functions) or matrices (e.g., solve eigenvectors) in arbitrary nnn number of servers (n≥2n \geq 2n≥2), and the nnn-party protocols ensure the security of the original data even if n−1n-1n−1 servers collude. All protocols we designed only need constant interaction rounds, and we demonstrate them under universally composability security. We believe that these protocols can provide a new basic tool for actual outsourced tasks.
We focus on how to securely outsource computation task to the cloud and propose a secure outsourcing multiparty computation protocol on lattice-based encrypted data in two-cloud-servers scenario. Our main idea is to transform the outsourced data respectively encrypted by different users' public keys to the ones that are encrypted by the same two private keys of the two assisted servers so that it is feasible to operate on the transformed ciphertexts to compute an encrypted result following the function to be computed. In order to keep the privacy of the result, the two servers cooperatively produce a custom-made result for each user that is authorized to get the result so that all authorized users can recover the desired result while other unauthorized ones including the two servers cannot. Compared with previous research, our protocol is completely noninteractive between any users, and both of the computation and the communication complexities of each user in our solution are independent of the computing function.
With the increasing interest in Secure Multi-Party Computation protocols (MPC), there have been several works such as the SPDZ1protocol that tackled this problem under a malicious security with dishonest majority attack model. However, most of these MPC efforts assume that the nodes running the computations are also supplying the inputs, which is not a realistic assumption for many real-life applications. In this paper, we extend the SPDZ protocol to enable clients outsource data and computation to the clouds while ensuring the correctness of the results, in addition to integrity and confidentiality of the input and output. We guarantee that the computation among nodes is done correctly by verifying their output’s Message Authentication Codes (MACs) at the end. Specifically, we delegate this task to an honest server. Our approach strives to minimize the burden on clients while enabling cheating detection even when assuming a malicious attack model with dishonest majority.
Abstract. In secure delegatable computation, computationally weak devices (or clients) wish to outsource their computation and data to an untrusted server in the cloud. While most earlier work considers the general question of how to securely outsource any computation to the cloud server, we focus on concrete and important functionalities and give the first protocol for the pattern matching problem in the cloud. Loosely speaking, this problem considers a text T that is outsourced to the cloud S by a client CT. In a query phase, clients C1,...,Cl runanefficientprotocol with the server S and the client CT in order to learn the positions at which a pattern of length m matches the text (and nothing beyond that). This is called the outsourced pattern matching problem and is highly motivated in the context of delegatable computing since it offers storage alternatives for massive databases that contain confidential data (e.g., health related data about patient history). Our constructions offer simulation-based security in the presence of semi-honest and malicious adversaries (in the random oracle model) and limit the communication in the query phase to O(m) bits plus the number of occurrences — which is optimal. In contrast to generic solutions for delegatable computation, our schemes do not rely on fully homomorphic encryption but instead uses novel ideas for solving pattern matching, based on efficiently solvable instances of the subset sum problem. 1
The engineering challenges involved in building large scale quantum computers, and the associated infrastructure requirements, mean that when such devices become available it is likely that this will be in limited numbers and in limited geographic locations. It is likely that many users will need to rely on remote access to delegate their computation to the available hardware. In such a scenario, the privacy and reliability of the delegated computations are important concerns. On the other hand, the distributed nature of modern computations has led to a widespread class of applications in which a group of parties attempt to perform a joint task over their inputs, e.g., in cloud computing. In this paper, we study the multi-client delegated quantum computation problem where we consider the global computation be made up of local computations that are individually decided by the clients. Each client part is kept secret from the server and the other clients. We construct a composable secure multi-client delegated quantum computation scheme from any composable secure single-client delegated quantum computation protocol and quantum authentication codes.