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Optimizations for Risk-Aware Secure Supply Chain Master Planning J.UCS Special Issue: Security in Information Systems

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Optimizations for Risk-Aware Secure Supply Chain Master Planning J.UCS Special Issue: Security in Information Systems Optimizations for Risk-Aware Secure Supply Chain Master Planning J.UCS Special Issue: Security in Information Systems Axel SchrÄopfer (SAP Research Karlsruhe, Germany [email protected]) Florian Kerschbaum (SAP Research Karlsruhe, Germany fl[email protected]) Christoph SchÄutz (SAP Research Karlsruhe, Germany [email protected]) Richard Pibernik (Supply Chain Management Institute, EBS Wiesbaden, Germany [email protected]) Abstract: Supply chain master planning strives for optimally aligned production, warehousing and transportation decisions across a multiple number of partners. Its execution in practice is limited by business partners' reluctance to share their vital business data. Secure Multi-Party Computation (SMC) can be used to make such collaborative computations privacy-preserving by applying cryptographic techniques. Thus, computation becomes acceptable in practice, but the performance of SMC re- mains critical for real world-sized problems. We assess the disclosure risk of the input and output data and then apply a protection level appropriate for the risk under the assumption that SMC at lower protection levels can be performed faster. This speeds up the secure computation and enables signi¯cant improvements in the supply chain. 1 Introduction Supply chain master planning (SCMP) strives for optimally aligned production, warehousing and transportation decisions across multiple partners. In practice, we can commonly observe a decentralized coordination mechanism (referred to as upstream planning) that usually only leads to local optima rather than to global supply chain optima [Dudek, Stadtler 2005]. At least in theory, optimal master plans can be generated for the whole supply chain if some planning unit has at its disposal all relevant information pertinent to the individual partners in the supply chain. It is, however, a well known fact that companies are typ- ically not willing to share sensitive private data (e.g. cost and capacity data) ([Pibernik, Sucky 2006, Pibernik, Sucky 2007]). They perceive the risk that the central planning unit or other parties misuse data to their disadvantage in order to obtain additional bene¯ts. The major obstacles to centralized master planning can be removed if a mech- anism for securely and privately computing the supply master plan is in place [Atallah et al. 2009]. A central planning unit, e.g. a 4th party logistics provider (4PL), could then determine globally optimal master plans and distribute these to the individual partners involved in the supply chain. To this end, Secure (Multi-Party) Computation (SMC) can be employed such that the relevant data does not need to be disclosed even to the central planning unit. This o®ers the ultimate level of protection, since no data sharing risk remains. In this paper we propose a framework for secure centralized supply chain master planning (SS- CMP). We introduce a basic model for centralized supply chain master planning and, from this, derive the relevant data a central planning unit requires to opti- mally coordinate manufacturing and transportation decisions. We then analyse this data with respect to its "criticality". Criticality refers to how sensitive cer- tain pieces of data are and how willing the di®erent partners will be to share this data. The criticality is determined by the perceived risks associated with data sharing and its prior public knowledge. In this context, risk can be characterized by the potential negative impact that occurs if a partner misuses the data to its own bene¯t and the likelihood for this to happen. We derive an overall criti- cality assessment for each data element that is relevant for supply chain master planning and use information about on the prior (public) knowledge of the data to determine an overall criticality score. This criticality score constitutes an in- put to secure computation of centralized supply chain master plans. We map criticality scores to protection levels which consist of certain technologies and parameters for SMC. Lower protection levels lead to faster SMC implementa- tions. We propose a mixed approach to SMC combining the di®erent protection levels in one implementation and propose modi¯cations to Linear Programming (LP) that optimize the e®ort involved by selecting the pivot element based on the protection level. We experimentally verify the e®ectiveness of the new algorithm. 2 Related Work Numerous works in the area of supply chain management exists on supply chain master planning as well as information sharing and collaboration in supply chains. In general, it is a well acknowledged fact that sharing relevant infor- mation and planning in a collaborative fashion can improve supply chain per- formance and mitigate the consequences of demand variability, especially with respect to the well-known bullwhip e®ect (see for example [Chen et al. 1999, Lee et al. 1997, Min et al. 2005, Yu et al. 2001]). With respect to supply chain master planning, numerous authors have proposed multi-stage models that can be utilized to coordinate planning activities across multiple locations and ¯rms (e.g. [Fleischmann, Meyr 2003, Jayaraman, Pirkul 2001, Shapiro 2001]). Various authors have stated that employing a centralized approach to master planning will lead to better results as compared to decentralized approaches that are most commonly employed in industry. [Simpson, ErengÄuc2001], for example, analyze the disadvantages of upstream coordination in comparison with cen- tralized coordination. They compute the average gap between centralized and upstream coordination for several test scenarios with varying cost parameters and demand patterns. Similar ¯ndings are reported in [Pibernik, Sucky 2007]. However, centralized supply chain planning has not been widely adopted in industry. [HolstrÄomet al. 2002] states: "it is di±cult, or maybe even impossi- ble, to get a large network consisting of independent companies to agree on and implement a centralised planning and control solution." Reluctance to- wards information sharing (a prerequisite for centralized master planning) has been identi¯ed as the main obstacle that inhibits centralized master planning ([Pibernik, Sucky 2006, Pibernik, Sucky 2007]). For this reason, alternative ap- proaches have been developed that either build on negotiation based coordina- tion ([Dudek, Stadtler 2005]) or hybrid forms ([Pibernik, Sucky 2006]). So far there has been no research on supply chain master planning based on mech- anisms that privacy preserving data sharing and computation. To the best of our knowledge the only approach to secure multi-party computation in the area of supply chain management can be found in [Atallah et al. 2009]. The authors develop secure protocols for a Collaborative Planning, Forecasting, and Replen- ishment (CPFR) process. Next to the fact that we, in our paper, consider a di®erent problem setting, a major distinction between the research presented in [Atallah et al. 2009] and our research is that they do not consider di®erent pro- tection levels for di®erent risks of data to be shared. They follow the approach to provide the highest protection for all data using a specially developed pro- tocol. Their protocols are two-party protocols, while we consider a multi-party problem. We will now review related work for SMC. SMC allows a set of n players, P = P1;:::;Pn, to jointly compute an arbitrary function of their private inputs, f(x1; : : : ; xn). The computation is privacy pre- serving, i.e. nothing else is revealed to a player than what is inferable by his private input and the outcome of the function. A cryptographic protocol is then run between the players in order to carry out the computation. Even if there are adversarial players, the constraints on correctness and privacy can be proven to hold under well stated settings. These settings consider the type of adversary as well as his computing power which can be bounded or unbounded. An adversary can be passive, i.e. following the protocol correctly but trying to learn more or he can be active, by arbitrarily deviating. For the two-party case it has been proven by Yao in [Yao 1982], that any arbitrary function is computable in privacy pre- serving fashion, using garbled binary circuits. This approach has been extended to the multi-party case in [Beaver et al. 1990, Goldreich et al. 1987]. Alternative approaches base on secret sharing schemes. A player's secret s is split into m shares which are then distributed to m players. Players can compute intermedi- ate results on the shares, and in the end a reconstruction is performed in order to receive the ¯nal result. Other approaches utilize semantically secure homo- morphic encryption (HE) [Damgºard,Jurik 2001], a public encryption scheme, where E(x) ¢ E(y) = E(x + y) and x cannot be deduced by E(x). Using the general approach leads to solutions that have high complexity and are therefore almost always not practically feasible [Li, Atallah 2006]. Thus, in order to get a practical solution, a dedicated protocol should be constructed. Atallah et al. constructed solutions for a couple of supply chain problems, e.g. planning, forecasting, replenishment, benchmarking, capacity allocation and e-auctions ([Atallah et al. 2009, Atallah et al. 2004, Atallah et al. 2003]). Their cryptographic protocols base on additive secret sharing, homomorphic encryption and garbled circuits. A contribution of Atallah et al. which is closely related to ours is that of secure linear programming [Li, Atallah 2006]. It uses the simplex method intro- duced by Dantzig in [Dantzig, Thapa 1997] to solve linear programs which get expressed as a matrix D. The method consists of two steps: selecting the pivot element drs and pivoting all elements dij of D over this element. The pivot step 0 sets the new value of dij, denoted dij, by d0 = 1 for i = r and j = s (pivot element) ij drs d0 = dij for i = r and j 6= s (pivot row) ij drs d0 = ¡dij for i 6= r and j = s (pivot column) ij drs d0 = dij ¡disdrj for i 6= r and j 6= s (all other elements): ij drs The method is repeated until the optimal solution of the LP is found (resp., it is stated that the problem is unbounded or infeasible).
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