User trust is an important factor in the success of recommendation systems, including Internet of Things (IoT)-based recommendation systems. However, such trust can be eroded in many different ways (e.g., unauthorized data modifications). Several privacy-preservation schemes have been designed for specific data and/or require strict assumptions (e.g., a private/secure communication channel between client-server and third-party authentication). However, these may limit their application in practice. Hence, in this paper we propose the Reversible Data Transform (RDT) algorithm based privacy-preserving data collection protocol. Our protocol allows us to achieve privacy preservation against beyond the scope processing and does not require a private channel or rely on a third-party authentication. Due to group formation, the disclosure probability of the internal disclosure attack will not be greater than 1/k. Similarly, the reversible privacy-preserving data mining approach protects beyond the scope processing. Findings from the experimentation demonstrates the utility of the proposed protocol and its potential to be deployed in a mobile app recommendation system.