Privacy-Aware Data Offloading for Mobile Devices in Edge Computing

Computing resource allocation strategy considering privacy protection mechanism in edge computing environment

“…Moreover, these three methods almost all show linear growth, the security of data distribution and transmission can be enhanced by increasing the number of tasks. When the number of computing tasks is 5, 10, 15, 20 and 25, the privacy entropy of this method is 14.17, 49.78, 59.32, 68.02 and 83.33 respectively, which is better than the methods based on [26] and [27]. When the number of tasks is the same, although the privacy entropy of method in literature [27] is similar to the calculated value of proposed method, the proposed method performs better in terms of time consumption.…”

“…Since the value of privacy entropy is related to data security, the higher the average privacy entropy value, the higher the security of data transmission. From the comparison of average privacy entropy calculated by different methods shown in Figure 9, it can be seen that in terms of average privacy entropy, the proposed method has better performance than the methods in [26] and [27]. When the number of computing tasks is 5, 10, 15, 20 and 25, the average privacy entropy of proposed method is 2.02, 3.97, 5.96, 6.23 and 8.83 respectively.…”

“…It can be seen from the figure that the system energy consumption of the proposed method in the worst case is also lower than 20 J, which is better than the two comparison methods. The energy consumption of the algorithm in [26] in the best case is lower than that in [27], which can reflect the advantage of task unloading in saving equipment energy consumption to a certain extent. Based on the joint allocation algorithm of computing and communication resources of reinforcement learning, the algorithm proposed in this paper jointly optimizes the data task unloading and power control strategy for each IoT device, which provides better performance optimization for the coordinated operation of the whole system.…”

Privacy and Energy Co-Aware Data Aggregation Computation Offloading for Fog-Assisted IoT Networks

“…Furthermore, for the sake of reducing service latency and energy consumption with security guarantee, the integration of privacy protection and computation offloading attracts great attention. For example, in [29], a privacy-aware data offloading method in edge computing was proposed to prohibit privacy leakage and achieve low latency. Similarly, He et al [30] developed a constrained Markov decision process based privacy-aware task offloading scheduling algorithm, which achieves the best possible delay and energy consumption with a high level of privacy.…”

“…The optimal values Updating the α i (k), β i (k) and R i (k) by formula (29) with momentum (30) and gradient function (31);…”