Enhancing Location Privacy Through Prioritized Experience Replay in Deep Q-Networks

The growth of location-based services (LBS) means increased privacy for individuals. Consequently, it re-quires a solid base for user privacy mechanisms. Our study proposes a decentralized method based on Deep Q-Networks (DQN), enhanced by Prioritized Experience Replay (PER), and applies Federated Learning (FL) for a better training process. PER, apart from being informational, aims mainly to provide an effective way to learn while on the other hand, Federated Learning relies on a centralized server to train the model using the replay memory but not at the expense of the privacy of the data during the learning process. After the trained model weights are successfully transferred to client devices, prediction can be performed locally and real-time decisions can be made as positions change. The model incorporates implicit cues presented by app context, frequency of use, and separation factors among others to estimate users' privacy attitude toward location data sharing. The test resourcefulness upholds a claim to the advantages of the augmented DQN model, as shown in the results in which PER and Federated Learning contribute to accelerated convergence and the enhanced ability to absorb information. As a result, there is the utilization of numerous tools that put this power into the hands of individual users in the ongoing development of LBS, hence, a detribalization of the digital space by empowerment.