A Distributed Secure Outsourcing Scheme for Solving Linear Algebraic Equations in Ad Hoc Clouds

Shen, Wenlong; Yin, Bo; Cao, Xianghui; Cheng, Yu; Shen, Xuemin

doi:10.1109/tcc.2016.2647718

Cited by 32 publications

(28 citation statements)

References 34 publications

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“…In this paper, we reduce the bias estimation problem to the solution of linear equations (LEs). Several algorithms have been devoted to the distributed solution of LEs, with focus on asynchronous implementations [23], [24], graph connectivity conditions [25], secure computing [26], to name a few. However, in these algorithms, each node needs to find all the entries of the vector of the unknowns, which, if employed in our problem, would require the nodes to know the network size.…”

Section: Introductionmentioning

confidence: 99%

Bias Estimation in Sensor Networks

Shi

Persis

Tesi

et al. 2020

IEEE Trans. Control Netw. Syst.

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This paper investigates the problem of estimating biases affecting relative state measurements in a sensor network. Each sensor measures the relative states of its neighbors and this measurement is corrupted by a constant bias. We analyse under what conditions on the network topology and the maximum number of biased sensors the biases can be correctly estimated. We show that for non-bipartite graphs the biases can always be determined even when all the sensors are corrupted, while for bipartite graphs more than half of the sensors should be unbiased to ensure the correctness of the bias estimation. If the biases are heterogeneous, then the number of unbiased sensors can be reduced to two. Based on these conditions, we propose some algorithms to estimate the biases.

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Section: Introductionmentioning

confidence: 99%

Bias Estimation in Sensor Networks

Shi

Persis

Tesi

et al. 2020

IEEE Trans. Control Netw. Syst.

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“…In [17], we conducted some pioneering studies on developing a distributed outsourcing scheme for solving an LAE problem securely in a consensus-based framework. While the work in [17] established some general principles for developing a secure distributed framework for solving LAE problem, it did not fully address some important issues. 1) The proposed algorithm in [17] considers a special case that each agent handles only one row of the LAE problem.…”

Section: Introductionmentioning

confidence: 99%

“…While the work in [17] established some general principles for developing a secure distributed framework for solving LAE problem, it did not fully address some important issues. 1) The proposed algorithm in [17] considers a special case that each agent handles only one row of the LAE problem. The case in which each node stores multiple rows is not well investigated.…”

Section: Introductionmentioning

confidence: 99%

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Securely Solving Linear Algebraic Equations in a Distributed Framework Enhanced With Communication-Efficient Algorithms

Yin

Shen

Cao

et al. 2020

IEEE Trans. Netw. Sci. Eng.

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Solving linear algebraic equations (a.k.a., an LAE problem) distributedly in a network with multiple agents has wide applications in distributed control, estimation and signal processing. A consensus-based distributed computing framework is studied in this paper. Specifically, each agent knows only a subproblem of the LAE, i.e., a subset of all equations, and then all agents apply a consensus-based algorithm to update their estimates of the correct solution of the LAE problem iteratively. Under certain conditions, it has been shown that all the estimates converge to the exact solution exponentially fast. However, such a distributed paradigm is vulnerable to malicious behaviors in an adversarial environment. In this paper, we indicate a number of security threats in this process, and thus develop robust computing solutions against those attacks. With particular attention to low storage overhead, we develop a new alternating projection method to enhance the consensus algorithm. Furthermore, we design an innovative misbehavior detection mechanism by exploiting the homomorphic signature technique. Our method can detect misbehaving agents without the common, yet sometimes infeasible, assumption of local good majority. Another significant contribution presented in this paper is an original component dropping approach for mitigating the communication overhead during the consensus process. From both theoretical and engineering perspectives, we study how the consensus can still be reached when a big portion of elements in exchanged message vectors are dropped. In our preliminary numerical results, roughly 80% data transmission can be reduced per epoch at the expense of 35% extra epochs to reach the consensus, implying 73% reduction in terms of communication overhead.

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“…Although theoretical guarantees of privacy can be achieved with FHE, current FHE schemes have very high computation cost, making them impractical for large-scale computations such as large-scale NLPs addressed in this paper. Other work designed secure outsourcing protocols for specific problems, such as linear programming [19], system of equations [20], distributed linear programming [17], quadratic programming [21], and linear regression [4]. Outsourcing basic mathematical computations has also been studied, such as matrix determinant computation [13], matrix inversion [14], and modular exponentiations [7].…”

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confidence: 99%

Privacy-Preserving Outsourcing of Large-Scale Nonlinear Programming to the Cloud

2018

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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The increasing massive data generated by various sources has given birth to big data analytics. Solving large-scale nonlinear programming problems (NLPs) is one important big data analytics task that has applications in many domains such as transport and logistics. However, NLPs are usually too computationally expensive for resource-constrained users. Fortunately, cloud computing provides an alternative and economical service for resourceconstrained users to outsource their computation tasks to the cloud. However, one major concern with outsourcing NLPs is the leakage of users private information contained in NLP formulations and results. Although much work has been done on privacy-preserving outsourcing of computation tasks, little attention has been paid to NLPs. In this paper, we for the first time investigate secure outsourcing of general large-scale NLPs with nonlinear constraints. A secure and efficient transformation scheme at the user side is proposed to protect users private information; at the cloud side, generalized reduced gradient method is applied to effectively solve the transformed large-scale NLPs. The proposed protocol is implemented on a cloud computing testbed. Experimental evaluations demonstrate that significant time can be saved for users and the proposed mechanism has the potential for practical use.

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A Distributed Secure Outsourcing Scheme for Solving Linear Algebraic Equations in Ad Hoc Clouds

Cited by 32 publications

References 34 publications

Bias Estimation in Sensor Networks

Bias Estimation in Sensor Networks

Securely Solving Linear Algebraic Equations in a Distributed Framework Enhanced With Communication-Efficient Algorithms

Privacy-Preserving Outsourcing of Large-Scale Nonlinear Programming to the Cloud

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