P<sup>3</sup>: Privacy-Preserving Scheme Against Poisoning Attacks in Mobile-Edge Computing
Ping Zhao, Haojun Huang, Xiaohui Zhao, Daiyu Huang
Abstract
Mobile-edge computing (MEC) has emerged to enable users to offload their location data into the MEC server, and at the same time, the MEC server executes the location-aware data processing to compute the statistical results about these collected locations. However, malicious users may deliberately generate poisoning locations and send these poisoning locations to the MEC server, aiming to poison the statistical results learned by the MEC server and even the other users' location privacy. Existing work concerning privacy preservation in MEC has not studied such poisoning attacks in MEC. Another line of somehow related work focused on poisoning attacks in a different scenario-adversarial machine learning. However, MEC exhibits different features with the machine learning settings, and thus, the privacy preservation against poisoning attacks in MEC faces significantly new challenges. To address the problem, we propose the privacy-preserving scheme, i.e., privacy-preserving scheme against poisoning (P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ), that utilizes the feature learning model to infer the social relationships among users from their location data and then constructs the inferred social graph. Thereafter, it searches the optimal map between the inferred social graph and the social graph from social networks to identify the poisoning locations. Experiments on two real-world data sets, two baseline works, and two kinds of poisoning attacks have demonstrated the privacy preservation against the poisoning attacks in MEC P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> provides.