Privacy-Preserving Computation Of Benchmarks On Item-Level Data Using Rfid

Currently, companies are about to optimize their internal processes by monitoring items they handle with Radio Frequency Identification (RFID). However, there is a risk that sensitive information is disclosed when sharing RFID data with other companies. Therefore, companies are unwilling to share RFID data. At first glance; Secure Multi-Party Computation (SMC) might reconciliate data sharing with the privacy concerns. However, SMC requires the collaboration of all parties involved in a protocol. This prevents using SMC for many applications based on item-level RFID data collected in supply chains, since some parties may be competitors or have conflicting interests. We present protocols for securely and privately computing item-level metrics using only existing communication links (e.g., messages stored on RFID tags) and an oblivious third party. This enables optimizing the supply chain using novel item-level metrics without compromising sensitive information.