User trust is the most important factor in the success of the IoT based recommendation system. This trust can be eroded in many ways but the most basic of them is through hacking attacks during data transmission or perform beyond the scope processing on the collected data. Existing studies have presented different privacy-preservation schemes to ensure the privacy of specific data and require strict assumptions which include a private/secure communication channel between client-server and third-party authentication. These issues can be mitigated through our proposed Reversible Data Transform (RDT) algorithm based privacy-preserving data collection protocol which ensures the privacy preservation against beyond the scope processing and it does not require a private channel and third-party authentication assumptions. 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. The experimentation on datasets advocates the good performance of the proposed protocol in the mobile app recommendation system (MARS) while considering its counterparts.

用户信任是基于物联网的推荐系统成功的最重要因素。这种信任可以通过多种方式削弱，但是最基本的方式是通过在数据传输期间进行黑客攻击或对收集的数据进行超出范围的处理。现有研究提出了不同的隐私保护方案，以确保特定数据的隐私，并要求严格的假设，其中包括客户端服务器与第三方身份验证之间的私有/安全通信通道。这些问题可以通过我们提出的基于可逆数据转换（RDT）算法的基于隐私保护的数据收集协议来缓解，该协议可确保隐私保护免受超出范围的处理，并且不需要专用通道和第三方身份验证假设。由于组建，1 /$ķ$。同样，可逆的隐私保护数据挖掘方法可提供超出范围处理范围的保护。对数据集的实验在考虑移动应用推荐系统（MARS）的同时，提倡该协议在移动应用推荐系统（MARS）中的良好性能。