Destination prediction plays an important role as the basis for a variety of location-based services (LBSs). However, it poses many threats to users’ location privacy. Most related work ignores privacy preservation in destination prediction. Few studies focus on specific kinds of privacy-preserving destination prediction algorithms and thus are not applicable to other prediction methods. Furthermore, the third party involved in these studies is a potential privacy threat. Additionally, another line of related work regarding LBSs neither guarantees the utility of the predicted results nor provides quantifiable privacy preservation. To this end, in this paper, we propose a general framework that can provide quantifiable privacy preservation and obtain a trade-off between the privacy and the utility of the predicted results by utilizing differential privacy and a neural network model. Specifically, it first adopts a specially designed differential privacy to construct a data-driven privacy-preserving model that formulates the relationship between injected noise and privacy preservation. Then, it combines a Recurrent Neural Network and Multi-hill Climbing to add fine-grained noise to obtain the trade-off between the privacy preservation and the utility of the predicted results. Our extensive experiments on real-world datasets validate that the proposed framework can be applied to different prediction methods, provide quantifiable location privacy preservation, and guarantee the utility of the predicted results simultaneously.

With the increasing popularity of location-based services (LBS), it is of paramount importance to preserve one’s location privacy. The commonly used location privacy preserving approach, location k-anonymity, strives to aggregate the queries of k nearby users within a so-called cloaked region (CR) via a trusted third-party anonymiser. As such, the probability to identify the location of every user involved is no more than 1/k, thus offering privacy preservation for users. One inherent limitation of k-anonymity, however, is that all users involved are assumed to be trusted and report their real locations. When location injection attacks (LIA) are conducted, where the untrusted users inject fake locations (along with fake queries) to the anonymiser, the probability of disclosing one’s location privacy could be greatly more than 1/k, yielding a much higher risk of privacy leakage. To tackle this problem, in this paper we present ILLIA, the first work that enables k-anonymity-based privacy preservation against LIA in continuous LBS queries. Central to the ILLIA idea is to explore the pattern of the users’ mobility in continuous LBS queries. With a thorough understanding of the users’ mobility similarity, a credibility-based k-anonymity scheme is developed, such that ILLIA is able to defense against LIA without requiring in advance knowledge of how fake locations are manipulated while still maintaining high quality of services. Both the effectiveness and the efficiency of ILLIA are validated by extensive simulations on real world dataset loc-Gowalla.

With the rapid developments of electric vehicles (EVs) in recent years, it is desirable to improve the energy efficiency to prolong the limited life of battery and extend the cruising range of EVs. In real EVs, the battery thermal management system is installed to cool the battery and maintain the expected high power output. In this article, we propose a novel energy management strategy based on deep reinforcement learning (DRL) considering battery thermal effects on energy efficiency. The main idea is to formulate energy management as an optimization problem, further extract the state sequence features of the vehicle via gated recurrent unit (GRU), and finally, propose a double deep $Q$ network (double DQN)-based algorithm to obtain the optimal strategy. Comparisons of our double DQN algorithm and existent fuzzy control, as well as two other conventional reinforcement learning (RL) algorithms, are conducted under New European Driving Cycle, FTP-75, HWFET, and US06 cycles, and the results demonstrate that the proposed algorithm achieves an energy reduction of more than 6.7% during aggressive driving.

The emergence of mobile edge computing (MEC) enables resource-limited user devices to run computation-intensive applications with the aid of edge server, but the untrustworthiness of the third-party edge server raises the leakage risk of users’ privacy. In this article, we consider an edge-cloud collaboration (ECC) scenario, consisting of multiple user devices with energy harvesting component, one edge server and one cloud server, and we concern about the issues of time latency, energy consumption, and privacy level of user devices in the process of task offloading. Specifically, we formulate the tradeoff between offloading cost and privacy level as a jointly optimization problem, and further model it as a Markov decision process (MDP). We then propose a privacy-preserving task offloading building upon the deep $Q$ -network (DQN), which enables user devices to make the optimal offloading decision to decrease delay, reduce energy cost, and enhance privacy level. Extensive simulation results prove that our method can reduce the offloading cost whilst boosting the privacy level of user devices compared to conventional reinforcement learning (RL) algorithm and two baselines.

Federated learning (FL) enables multiple worker devices share local models trained on their private data to collaboratively train a machine learning model. However, local models are proved to imply the information about the private data and, thus, introduce much vulnerabilities to inference attacks where the adversary reconstructs or infers the sensitive information about the private data (e.g., labels, memberships, etc.) from the local models. To address this issue, existing works proposed homomorphic encryption, secure multiparty computation (SMC), and differential privacy methods. Nevertheless, the homomorphic encryption and SMC-based approaches are not applicable to large-scale FL scenarios as they incur substantial additional communication and computation costs and require secure channels to delivery keys. Moreover, differential privacy brings a substantial tradeoff between privacy budget and model performance. In this article, we propose a novel FL framework, which can protect the data privacy of worker devices against the inference attacks with minimal accuracy cost and low computation and communication cost, and does not rely on the secure pairwise communication channels. The main idea is to generate the lightweight keys based on computational Diffie–Hellman (CDH) problem to encrypt the local models, and the FL server can only get the sum of the local models of all worker devices without knowing the exact local model of any specific worker device. The extensive experimental results on three real-world data sets validate that the proposed FL framework can protect the data privacy of worker devices, and only incurs a small constant of computation and communication cost and a drop in test accuracy of no more than 1%.