The newly emerged machine learning (e.g., deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both friend and foe. Currently, the work on the preservation of privacy and machine learning are still in an infancy stage, as most existing solutions only focus on privacy problems during the machine learning process. Therefore, a comprehensive study on the privacy preservation problems and machine learning is required. This article surveys the state of the art in privacy issues and solutions for machine learning. The survey covers three categories of interactions between privacy and machine learning: (i) private machine learning, (ii) machine learning-aided privacy protection, and (iii) machine learning-based privacy attack and corresponding protection schemes. The current research progress in each category is reviewed and the key challenges are identified. Finally, based on our in-depth analysis of the area of privacy and machine learning, we point out future research directions in this field.

Millimetre wave (mmWave) is a promising technology to meet the ever-growing data traffic in the future. A major challenge of mmWave communications is the high path loss. In order to overcome this issue, mmWave systems often adopt beamforming techniques, which require robust channel estimation and beam tracking algorithms to maintain an adequate quality of service. This paper proposes a framework of channel estimation and beam tracking for mmWave communications. The proposed framework is designed for vehicular to infrastructure communication but can be extended to other applications as well. First, we propose a multi-stage adaptive channel estimation algorithm called robust adaptive multi-feedback (RAF). The algorithm is based on using the estimated channel coefficient to predict a lower bound for the required number of measurements. Our simulations demonstrate that compared with the existing algorithms, RAF can achieve the desired probability of estimation error (PEE), while on average reducing the feedback overhead by 75.5% and the total channel estimation time by 14%. Second, after estimating the channel in the first step, the paper follows by investigating the extended Kalman filter (EKF) for beam tracking in vehicular communications. A crucial part of EKF is the calculation of Jacobian matrices. We show that the model used in the previous work, which was based on the angles of arrival and departure, is not suitable for vehicular communications. This is due to the complexity in the calculation of Jacobian matrices. A new model is proposed for EKF in mmWave vehicular communications which is based on position, velocity and channel coefficient. Closed-form expressions are derived for the Jacobians used in EKF which facilitate the implementation of the EKF tracking algorithm in the proposed model. Finally, we provide an extensive number of simulations to substantiate the robustness of the framework as well as presenting the analytical results on the PEE of the RAF algorithm.Index Terms-Millimeter wave, multiple-input multiple-output (MIMO), channel estimation, beamforming, analog beamforming, beam tracking, Extended Kalman filter (EKF).

Publishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users' private information. One of the most sensitive sources of data is spatiotemporal trajectory datasets. Unfortunately, merely removing unique identifiers cannot preserve the privacy of users. Adversaries may know parts of the trajectories or be able to link the published dataset to other sources for the purpose of user identification. Therefore, it is crucial to apply privacy preserving techniques before the publication of spatiotemporal trajectory datasets. In this paper, we propose a robust framework for the anonymization of spatiotemporal trajectory datasets termed as machine learning based anonymization (MLA). By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use k-means algorithm for this purpose. A variation of k-means algorithm is also proposed to preserve the privacy in overly sensitive datasets. Moreover, we improve the alignment process by considering multiple sequence alignment as part of the MLA. The framework and all the proposed algorithms are applied to TDrive and Geolife location datasets. The experimental results indicate a significantly higher utility of datasets by anonymization based on MLA framework.

In this paper, we present an introductory course design for fresh research-based graduate students in wireless communications, which is planned to be delivered immediately after enrollment. The course aims at covering necessary research methodologies and the rudiments of wireless communications. Different from most graduate study curricula based on experiments, we design the course structure in an introductory manner by tailoring the course contents to a simplistic form and involving only simulations. We start by introducing the pedagogical background and our motivations for designing this course. Then, we present the fundamentals and the technological contents, followed by detailing the basic research methodology, strategies, and skills for deriving insightful analytical expressions and carrying out numerical simulations. We also verify the effectiveness of the course structure design by carrying out a teaching experiment and analyzing objective and subjective data collected from the experiment. By participating in this course, research-based graduate students are expected to gain a preliminary knowledge of the research methodology and strategies in the field of wireless communications as well as a research roadmap. INDEX TERMS Wireless communications, research methodology, graduate students, telecommunications education, teaching experiment.

Recent years have seen rising needs for location-based services in our everyday life. Aside from the many advantages provided by these services, they have caused serious concerns regarding the location privacy of users. Adversaries can monitor the queried locations by users to infer sensitive information, such as home addresses and shopping habits. To address this issue, dummy-based algorithms have been developed to increase the anonymity of users, and thus, protecting their privacy. Unfortunately, the existing algorithms only assume a limited amount of side information known by adversaries, which may face more severe challenges in practice. In this paper, we develop an attack model termed as Viterbi attack, which represents a realistic privacy threat on user trajectories. Moreover, we propose a metric called transition entropy that enables the evaluation of dummy-based algorithms, followed by developing a robust algorithm that can defend users against the Viterbi attack while maintaining significantly high performance in terms of the traditional metrics. We compare and evaluate our proposed algorithm and metric on a publicly available dataset published by Microsoft, i.e., Geolife dataset.

Most online mobile services make use of location data to improve customer experience. Mobile users can locate points of interest near them, or can receive recommendations tailored to their whereabouts. However, serious privacy concerns arise when location data is revealed in clear to service providers. Several solutions employ Searchable Encryption (SE) to evaluate spatial predicates directly on location ciphertexts. While doing so preserves privacy, the performance overhead incurred is high. We focus on a prominent SE technique in the publickey setting -Hidden Vector Encryption (HVE), and propose a graph embedding technique to encode location data in a way that significantly boosts the performance of processing on ciphertexts. We show that finding the optimal encoding is NP-hard, and provide several heuristics that are fast and obtain significant performance gains. Our extensive experimental evaluation shows that our solutions can improve computational overhead by a factor of two compared to the baseline.

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The Smart Grid, acting as a powerful technique for addressing the existing challenges in the power network, has matured in recent years. Its importance has been well recognised. As the most important property of the Smart Grid, the information interactivity over the power network is a frequent topic for academia and industry. Therefore this paper introduces the main achievements of recent years associated with the communication technology and signal processing methodology based on the Smart Grid. Their pros and cons will also be summarised and analysed in detail. To be brief, the novel properties and developments of the Smart Grid, the communication techniques, and the involvement of cRIO integrating FPGA, TDMS, SQL Server, DataSocket Server and LabVIEW Electrical Power Suite will be elaborated upon.