Secure supply-chain protocols

Atallah, Mikhail J.; Elmongui, Hicham G.; Deshpande, Vinayak; Schwarz, Leroy B.

doi:10.1109/coec.2003.1210264

Cited by 60 publications

(44 citation statements)

References 21 publications

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“…Another example is given by Atallah et al (2003), who address capacity allocation and auctions-a supplier-consumer interaction rather than the peer-to-peer interaction presented in this paper. An interesting open research question concerns the potential trade-off between the use of a heuristic approach to problem solving that admits a tight security property versus a closer-to-optimal algorithm that permits data leakage.…”

Section: Discussionmentioning

confidence: 99%

An Approach to Securely Identifying Beneficial Collaboration in Decentralized Logistics Systems

Clifton

Iyer

Cho

et al. 2008

M&SOM

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T he problem of sharing manufacturing, inventory, or capacity to improve performance is applicable in many decentralized operational contexts. However, the solution of such problems commonly requires an intermediary or a broker to manage information security concerns of individual participants. Our goal is to examine use of cryptographic techniques to attain the same result without the use of a broker. To illustrate this approach, we focus on a problem faced by independent trucking companies that have separate pick-up and delivery tasks and wish to identify potential efficiency-enhancing task swaps while limiting the information they must reveal to identify these swaps. We present an algorithm that finds opportunities to swap loads without revealing any information except the loads swapped, along with proofs of the security of the protocol. We also show that it is incentive compatible for each company to correctly follow the protocol as well as provide their true data. We apply this algorithm to an empirical data set from a large transportation company and present results that suggest significant opportunities to improve efficiency through Pareto improving swaps. This paper thus uses cryptographic arguments in an operations management problem context to show how an algorithm can be proven incentive compatible as well as demonstrate the potential value of its use on an empirical data set.

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Section: Discussionmentioning

confidence: 99%

An Approach to Securely Identifying Beneficial Collaboration in Decentralized Logistics Systems

Clifton

Iyer

Cho

et al. 2008

M&SOM

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“…Atallah et al have proposed Secure Supply-Chain Collaboration (SSCC) problem, and developed SSCC protocols for simple e-Auction scenarios and simple capacity-allocation problem [3]. Our secure collaborative forecasting and benchmarking can be viewed as a branch of the SSCC problem.…”

Section: Related Workmentioning

confidence: 99%

Private collaborative forecasting and benchmarking

Atallah

Bykova

et al. 2004

Proceedings of the 2004 ACM Workshop on Privacy in the Electronic Society

111

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Suppose a number of hospitals in a geographic area want to learn how their own heart-surgery unit is doing compared with the others in terms of mortality rates, subsequent complications, or any other quality metric. Similarly, a number of small businesses might want to use their recent point-of-sales data to cooperatively forecast future demand and thus make more informed decisions about inventory, capacity, employment, etc. These are simple examples of cooperative benchmarking and (respectively) forecasting that would benefit all participants as well as the public at large, as they would make it possible for participants to avail themselves of more precise and reliable data collected from many sources, to assess their own local performance in comparison to global trends, and to avoid many of the inefficiencies that currently arise because of having less information available for their decision-making. And yet, in spite of all these advantages, cooperative benchmarking and forecasting typically do not take place, because of the participants' unwillingness to share their information with others. Their reluctance to share is quite rational, and is due to fears of embarrassment, lawsuits, weakening their negotiating position (e.g., in case of over-capacity), revealing corporate performance and strategies, etc. The development and deployment of private benchmarking and forecasting technologies would allow such collaborations to take place without revealing any participant's data to the others, reaping the benefits of collaboration while avoiding the drawbacks. Moreover, this kind of technology would empower smaller organizations who could then cooperatively base their decisions on a much broader information base, in a way that is today restricted to only the largest corporations. This paper is a step towards this goal, as it gives protocols for forecasting and benchmarking that reveal to the participants the desired answers yet do not reveal to any participant any other participant's private data. We consider several forecasting methods, including linear regression and time series techniques such as moving average and exponential smoothing. One of the novel parts of this work, that further distinguishes it from previous work in secure multi-party computation, is that it involves floating point arithmetic, in particular it provides protocols to securely and efficiently perform division.

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“…TTP is responsible for enforcing owner policies on the shared information. Substantial studies have been done to address information sharing and access control in the area of collaborative design in [11,12] and supply chain in [2,13,14].…”

Section: Related Workmentioning

confidence: 99%

Protecting PLM Data Throughout Their Lifecycle

Ranchal

Bhargava

2013

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

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Abstract. Enterprises operate in a global economy with their operations dispersed across internal processes and external partners. Product Lifecycle Management (PLM) systems play a significant role in modern product development and management. There are multiple stages in product lifecycle that streamline by sharing data among PLM entities. Shared data may contain highly sensitive information such as trade secrets, intellectual property, private organizational or personal information. In large enterprise systems, it is difficult to understand and track data dissemination. Data sharing across global partners complicates and magnifies the problem further. The effect of shared data being leaked is one of the key risks. Existing approaches ensure security within the domain of an organization and don't address protection in a decentralized environment. We propose an approach for secure data dissemination using the Active Bundle scheme. This approach enables organizations to securely share information in their PLM steps and protects it throughout the product lifecycle.

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Secure supply-chain protocols

Abstract: Abstract

Cited by 60 publications

References 21 publications

An Approach to Securely Identifying Beneficial Collaboration in Decentralized Logistics Systems

An Approach to Securely Identifying Beneficial Collaboration in Decentralized Logistics Systems

Private collaborative forecasting and benchmarking

Protecting PLM Data Throughout Their Lifecycle

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