Secure and efficient distributed linear programming

Yuan, Hong; Vaidya, Jaideep; Lu, Haibing

doi:10.3233/jcs-2012-0452

Cited by 28 publications

(33 citation statements)

References 36 publications

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“…Such information disclosure would explicitly compromise the consumers' privacy on the power grid [40,41]. We will explore privacy-preserving schemes [42,43] to effectively formulate and efficiently solve the ECS/GECS problem among multiple agents on the smart grid with limited disclosure [44,45]. Furthermore, we will extend the agent-based ECS problems for appliances to the entities with renewable energy sources [46,47], considering each agent and its appliances as a microgrid (which both consumes and generates electricity).…”

Section: Discussionmentioning

confidence: 99%

Efficient Energy Consumption Scheduling: Towards Effective Load Leveling

2017

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Different agents in the smart grid infrastructure (e.g., households, buildings, communities) consume energy with their own appliances, which may have adjustable usage schedules over a day, a month, a season or even a year. One of the major objectives of the smart grid is to flatten the demand load of numerous agents (viz. consumers), such that the peak load can be avoided and power supply can feed the demand load at anytime on the grid. To this end, we propose two Energy Consumption Scheduling (ECS) problems for the appliances held by different agents at the demand side to effectively facilitate load leveling. Specifically, we mathematically model the ECS problems as Mixed-Integer Programming (MIP) problems using the data collected from different agents (e.g., their appliances' energy consumption in every time slot and the total number of required in-use time slots, specific preferences of the in-use time slots for their appliances). Furthermore, we propose a novel algorithm to efficiently and effectively solve the ECS problems with large-scale inputs (which are NP-hard). The experimental results demonstrate that our approach is significantly more efficient than standard benchmarks, such as CPLEX, while guaranteeing near-optimal outputs.

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Section: Discussionmentioning

confidence: 99%

Efficient Energy Consumption Scheduling: Towards Effective Load Leveling

2017

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“…Du [12] and Vaidya [13] transformed the linear programming problem by multiplying a monomial matrix to both the constraint matrix and the objective function, assuming that one party holds the objective function while the other party holds the constraints. Bednarz et al [14,15] pointed out a potential attack to the above transformation approach, which has been resolved in [3,16]. In addition, Mangasarian presented two transformation approaches for horizontally partitioned linear programs [17] and vertically partitioned linear programs [18] respectively.…”

Section: Related Workmentioning

confidence: 99%

“…The reason for this is the high degree of trust required, wherein proprietary data has to be shared with an external party which can then carry out the optimization. To deal with this, privacy-preserving solutions have been developed to enable collaborative optimization for several specific problems [1][2][3][4][5]. In this paper, we focus on the traveling salesman problem (TSP).…”

Section: Introductionmentioning

confidence: 99%

Collaboratively Solving the Traveling Salesman Problem with Limited Disclosure

Yuan

Vaidya

et al. 2014

Lecture Notes in Computer Science

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Abstract. With increasing resource constraints, optimization is necessary to make the best use of scarce resources. Given the ubiquitous connectivity and availability of information, collaborative optimization problems can be formulated by different parties to jointly optimize their operations. However, this cannot usually be done without restraint since privacy/security concerns often inhibit the complete sharing of proprietary information. The field of privacy-preserving optimization studies how collaborative optimization can be performed with limited disclosure. In this paper, we develop privacy-preserving solutions for collaboratively solving the traveling salesman problem (TSP), a fundamental combinatorial optimization problem with applications in diverse fields such as planning, logistics and production. We propose a secure and efficient protocol for multiple participants to formulate and solve such a problem without sharing any private information. We formally prove the protocol security under the rigorous definition of secure multiparty computation (SMC), and demonstrate its effectiveness with experimental results using real data.

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“…Mangasarian [12] uses only the multiplication with P for a system with only equality constraints (2). Hong et al [8] propose a complex set of protocols for a certain kind of distributed linear programming problems. Regarding the security, they prove that these protocols leak no more than what is made public by Bednarz's transformation.…”

Section: Overview Of Proposed Methodsmentioning

confidence: 99%

“…Transformation-based methods have been proposed in [4,3,14,11,12,15,8,2,10,7]. A set of "standard" transformations, applicable to the initial program, have been proposed over the years.…”

Section: Transformation Methodsmentioning

confidence: 99%

New Attacks against Transformation-Based Privacy-Preserving Linear Programming

Laud

Pankova

2013

Security and Trust Management

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In this paper we demonstrate a number of attacks against proposed protocols for privacypreserving linear programming, based on publishing and solving a transformed version of the problem instance. Our attacks exploit the geometric structure of the problem, which has mostly been overlooked in the previous analyses and is largely preserved by the proposed transformations. The attacks are efficient in practice and cast serious doubt to the viability of transformation-based approaches in general.

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Secure and efficient distributed linear programming

Cited by 28 publications

References 36 publications

Efficient Energy Consumption Scheduling: Towards Effective Load Leveling

Efficient Energy Consumption Scheduling: Towards Effective Load Leveling

Collaboratively Solving the Traveling Salesman Problem with Limited Disclosure

New Attacks against Transformation-Based Privacy-Preserving Linear Programming

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