基于k-匿名的隐私保护计算卸载方法

针对移动边缘计算(MEC)中用户的卸载任务及卸载频率可能使用户被攻击者锁定的问题，该文提出一种基于k-匿名的隐私保护计算卸载方法。首先，该方法基于用户间卸载任务及其卸载频率的差异性，提出隐私约束并建立基于卸载频率的隐私保护计算卸载模型；然后，提出基于模拟退火的隐私保护计算卸载算法(PCOSA)求得最优的k-匿名分组结果和组内各任务的隐私约束频率；最后，在卸载过程中改变用户原始卸载频率满足隐私约束，最小化终端能耗。仿真结果表明，PCOSA算法能找出用户所处MEC节点下与用户卸载表现最相近的k个用户形成匿名集，有效保护了所有用户隐私。

A Privacy-preserving Computation Offloading Method Based on k-Anonymity

Users’ offloading tasks and offloading frequencies in Mobile Edge Computing(MEC) may cause users to be locked out. A privacy-preserving computation offloading method based on k-anonymity is proposed in this paper. Firstly, based on the differences between offloading tasks and their frequencies, privacy constraint is proposed to establish a privacy-preserving computation offloading model based on offloading frequency; Then, a Privacy-preserving Computation Offloading algorithm based on Simulated Annealing (PCOSA) is utilized to obtain the optimal k-anonymous groups and the privacy constraint frequency of each task; Finally, the user’s original offloading frequencies are changed to meet the privacy constraint while minimizing terminal energy consumption. Simulation results validate that the PCOSA can find out k users with the closest offloading performance to form anonymous sets, which protects effectively the privacy of all users.