Hiding Sensitive Information when Sharing Distributed Transactional Data

Although retailers recognize the potential value of sharing transactional data with supply chain partners, many remain reluctant to share. However, there is evidence that the extent of sharing would be greater if information sensitive to retailers can be concealed before sharing. Extant research has only considered sensitive information at the organizational level. This is rarely the case in reality; the retail industry has adapted their offerings to region-wide differences in customer tastes for decades. Differences in customer characteristics across regions lead to region-specific sensitive information in addition to any at the organizational level. This is the first paper to propose an approach to solve this version of the problem. Region-level requirements increase the size of an already difficult (NP-hard) problem substantially, making adaptations of existing approaches impractical. We present an ensemble approach that draws intuition from Lagrangian relaxation to conceal sensitive patterns at the organizational and regional levels with minimal damage to the data set. Extensive computational experiments show that it identifies optimal or near-optimal solutions even when other approaches fail, doing so without any loss in recommendation effectiveness. This mitigates potential risks associated with sharing and should increase data sharing among partners in the supply chain.

• 저널Information Systems Research · 31(2) · 473–490
• 토픽Consumer Market Behavior and Pricing · Marketing
• DOI10.1287/isre.2019.0898
• 저자Abhijeet Ghoshal, Jing Hao, Syam Menon, Sumit Sarkar