A Privacy Preservation Model for RFID Data-Collections is Highly Secure and More Efficient than LKC-Privacy

Abstract

RFID is a smart label technology that is used in several real-life applications such as inventory management, asset tracking, personnel tracking, controlling access to restricted areas, ID badging, supply chain management, counterfeit prevention (e.g., in the pharmaceutical industry), and smart farming. Generally, the data collection of RFIDs consists of the users’ visited locations and their visiting time, so called as trajectory datasets. Aside from applications, trajectory datasets can also be released for public use. For this reason, they could lead to being privacy violation issues. To address these issues in trajectory datasets, LKC-Privacy is proposed. Unfortunately, in this work, we demonstrate that LKC-Privacy still has a serious vulnerability that must be improved. To rid the demonstrated vulnerability of LKC-Privacy, a privacy preservation model is proposed in this work. Furthermore, the proposed mode is evaluated by extensive experiments. From the experimental results, they indicate that the proposed model is highly secure and more efficient than LKC-Privacy.