A pipeline approach for privacy preservation against poisoning attacks in a Mobile Edge Computing environment

Abstract

Mobile Edge Computing (MEC) has become increasingly popular due to its ability to provide real-time data processing and improved user experience. However, it also introduces potential security vulnerabilities, as malicious actors could exploit users’ position data stored in the MEC server. To address this challenge, it is essential to develop privacy-preserving schemes that are resistant to malicious activities, such as poisoning attacks. Previous works have not considered the evaluation of such schemes against certain types of poisoning attacks while ensuring sufficient utility, privacy, and scalability during the generation process of poisoning locations. Indeed, meeting the needs of the user or customers, keeping data secure and confidential, and having capacity to accommodate changes in demand are essential for any application or system to operate efficiently and securely. To address this limitation, we present a new pipeline approach for a privacy-preserving scheme against poisoning attacks in the MEC environment. We evaluate this method with a type of poisoning attack that ensures sufficient utility, privacy, and scalability during the generation process and assess its effectiveness in providing security and privacy. Our approach is evaluated on two data sets and the results are compared with three baseline approaches. The results show that our approach is more effective in detecting poisoning locations than the existing works without affecting the QoS metrics (bandwidth, resources consumption, latency and the storage) with should be consider when evaluating MEC method.