Xception Convolutional Scaling Wide Residual Network: A Robust Anonymization Framework for Location Privacy in Peer‐to‐Peer Systems

ABSTRACT Location‐based services (LBS) offer useful information based on a user's location, but they raise privacy risks since sensitive data can be misused by third parties. Traditional peer‐to‐peer (P2P) systems try to protect privacy but struggle to balance anonymization with service accuracy, and the problem worsens as the number of users and queries increases. These issues are overcome by introducing a novel Xception Convolutional Scaling Wide Residual Network (XCovSWideR‐Net) model to improve location privacy in P2P systems through anonymization. The anonymization process involves the user, an anonymization server, and the LBS server. The anonymizer first hides the user's personal and location details and then adds dummy locations to mask the real query. Privacy is further improved using the XCovSWideR‐Net model, which combines Xception Convolutional Network (XCovNet) and Scaling Wide Residual Network (SwideRes‐Net). The anonymized query is sent to the LBS server, which returns the requested information to the anonymization server, and finally to the user without revealing their actual location. The XCovWideR‐Net model achieved maximum location privacy of 0.967, location preservation of 0.974, anonymous entropy of 8.268, and a minimum computation time of 2.480 s for 800 users in Scenario 3. These findings highlight the ability of the proposed method to effectively balance privacy, accuracy, and efficiency, providing a promising solution for secure and scalable LBS applications.