Mohammadreza Mollahosini Ardakani, Fatemeh Zahedi, Zahra Zahedi

Abstract

Continuous developments in mobile networks and positioning technologies have created a strong market pressure for location-based services (LBS). Examples include location-based emergency services, location-based service ads, and location-sensitive billing. One of the major challenges in deploying LBS systems extensively is the privacy of location-based data. Without security, the widespread deployment of location-based services endangers the privacy of mobile users and exposes significant vulnerabilities to exploitation. In this article, we describe a customizable identity model to protect the privacy of location data. Our model has two unique features. First, we provide a customizable framework for supporting variable naming with the k variable, allowing a wide range of users to take advantage of location privacy security with personalized personalization requirements. Second, we design and develop a spatial and temporal hidden cipher algorithm called Clique Cloak, which provides location anonymity to LBS provider mobile users. The secrecy algorithm is run by a location protection broker on a trusted server, which hides node-related messages by hiding location information in messages to reduce or prevent privacy threats before sending them to the LBS provider. Makes cell phones anonymous. Our model enables each message sent from a mobile node to specify the level of anonymity that is desired as well as the maximum time and space tolerance needed to maintain the anonymity. We generate the effect of the undercover algorithm under artificial conditions using realistic artificial location data using real road maps and traffic volume data. Our experiments show that the k-anonymity location model with multi-dimensional secrecy and the k-adjustment parameter can obtain a high guarantee of anonymity k and high resistance to location privacy threats without significant performance penalty. The results shows after presenting the proposed solution, the mobile dataset was introduced along with the criteria for deviation ratio, execution and prediction accuracy. Providing information and predictive accuracy has improved significantly‎.