Privacy-preserving task-oriented semantic communication method and system

A method for privacy-preserving a task-oriented semantic communication comprising a user, an edge server, and a potential adversary;

• wherein a computer readable medium operable on a computer with memory for protecting privacy while executing tasks with low latency on the edge side, and comprising program instructions for executing the following steps of:
• (i) constructing a privacy-preserving task-oriented semantic communication system model;
• (ii) constructing an objective function based on the established semantic communication system model from step (i) and the privacy requirements;
• (iii) reconstructing the objective function constructed in step (ii) using variational approximations, Monte Carlo sampling, and reparameterization techniques;
• (iv) designing an adversarial learning mechanism to train the semantic communication system model based on the transformed objective function from step (iii);
• (v) performing task-oriented semantic communication using the trained semantic communication system model obtained from step (4), which comprises:
• the transmitter extracting and transmitting task-relevant semantic information from the image input;
• transmitting the semantic information through a wireless channel;
• and the receiver completing task inference based on the received signal;
• and
• (vi) improving privacy in the task-oriented semantic communication;
• wherein the constructed objective function is (IV):
• min
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• (
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• where LIB is the information bottleneck loss, LMSE is the mean square error loss, and λ is the trade-off parameter between privacy and edge inference performance, with a range of [0,1];
• in step (iii), the objective function constructed in step (ii) is reconstructed and expressed as (V):
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• "\[LeftBracketingBar]"
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• where LVIB is the information bottleneck loss, LMSE is the mean square error loss, λ is the trade-off parameter between privacy and edge inference performance, si is i-th sample of image dataset, and ŝi is a reconstruction of si;
• M stands for a total of M pairs (si, yi), N is the number of samples taken from the noise channel for each pair (si, yi), β is a constant, and KL (·∥·) is the divergence, which is used to calculate the difference between two distributions;
• in step (iv), based on the objective function reconstructed in step (iii), adversarial learning mechanism is designed to train the semantic communication system model;
• the specific training steps are as follows:
• a) randomly initialize the parameters of the semantic communication system model;
• b) the training data is input into the semantic communication system model, the training data is image data, which is divided into S1 and S2;
• c) simulate potential adversary, namely training data reconstruction module;
• d) simulate the user, train the transmitter network;
• e) steps c) and d) are performed alternately until the termination condition is met, then output the parameters θ and ϕ of the semantic communication system model;
• in step c), the specific implementation process includes:
• A) the transmitter network extracts and transmits task-related semantic information;
• the specific is as follows:
• Tθ(si)→1;
• s1 is the training data, from the S1;
• 1 is the extracted feature of the transmitter network;
• B) 1 is transmitted over wireless channels;
• C) the data reconstruction module reconstructs the original input based on the received 21;
• the specific is as follows:
• Dγ(1)→ŝ1;
• 1 is the representation of 1 after passing through the wireless channel, and ŝ1 is the recovery of s1;
• D) calculate the mean square error loss based on (VI):
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• where LMSE is the mean square error loss, M indicates the number of sample, s1,i is the i-th sample in the input image dataset s1, and ŝ1,i is the recovery of s1,i;
• E) the transmitter network parameters are frozen, and the Adam optimizer is used to update the network parameter γ of the data reconstruction module;
• in step e), the specific implementation process is as follows:
• A) the transmitter network extracts and transmits task-related semantic information, the specific is as follows:
• Tθ(S2)→2;
• s2 is the training data, from the S2 and 2 is the feature extracted by the transmitter network;
• B) 2 is transmitted over wireless channels;
• C) the task inference module performs task inference based on the received 2;
• the specific is as follows:
• Rϕ(2)→ŷ2 the data reconstruction module reconstructs the original input based on the received;
• the specific is as follows:
• Dγ(2)→ŝ2;
• 2 is the representation of 2 after passing through the wireless channel, and ŝ2 is the recovery of ŝ2;
• ŷ2 is the output of the task inference module;
• D) calculate the loss based on (V);
• E) the data remodeling network parameters are frozen, and the transmitter network parameters and the task inference module network parameters are updated through the Adam optimizer.