A Survey on the Privacy-Preserving Applications of Secure Transformation in Collaborative Supply Chains

Abstract

Collaboration is a key technique in modern supply chains, both for building trust with other companies, but also for reducing costs or maximizing profits. It is an approach which provides all involved parties with benefits that they could not possibly achieve on their own. Collaboration, however, requires abundant information, including proprietary information which the owners might not want to disclose publicly. This leads to the main privacy concern, namely ensuring the privacy of proprietary information, since access to this information can mean a competitive advantage in the market. Several techniques which enable collaboration while preserving privacy have been developed over time, including secure transformation, which is a non-cryptographic approach. The main focus of this paper is studying this technique and its recent developments, along with the feasibility of using it to preserve privacy in supply chains, through a literature review. Secure transformation is still somewhat in its infancy, with much theoretical research being conducted, yet the technique still not being employed in practice. Therefore, reviewing the research done is the most suitable approach to answering the question of how secure transformation applications can preserve privacy in collaborative supply chains. The main result of this research is that secure transformation is a double-edged sword which promises effective computation for certain collaborative problems, with the downside of having weaker security guarantees compared to cryptographic approaches.