As a prominent early instance of the IoT in the smart grid, the advanced metering infrastructure (AMI) provides real-time information from smart meters to both grid operators and customers, exploiting the full potential of demand response. However, the newly-collected information without security protection can be maliciously altered and result in huge loss. In this paper, we propose an energy theft detection scheme with energy privacy preservation in the smart grid. Specially, we use combined convolutional neural networks (CNN) to detect abnormal behavior of the metering data from a long-period pattern observation. In addition, we employ Paillier algorithm to protect the energy privacy. In other words, the users’ energy data are securely protected in the transmission and the data disclosure is minimized. Our security analysis demonstrates that in our scheme data privacy and authentication are both achieved. Experimental results illustrate that our modified CNN model can effectively detect abnormal behaviors at an accuracy up to 92.67%.

Examination of speech datasets for detecting dementia, collected via various speech tasks, has revealed links between speech and cognitive abilities. However, the speech dataset available for this research is extremely limited because the collection process of speech and baseline data from patients with dementia in clinical settings is expensive. In this paper, we study the spontaneous speech dataset from a recent ADReSS challenge, a Cookie Theft Picture (CTP) dataset with balanced groups of participants in age, gender, and cognitive status. We explore state-of-the-art deep transfer learning techniques from image, audio, speech, and language domains. We envision that one advantage of transfer learning is to eliminate the design of handcrafted features based on the tasks and datasets. Transfer learning further mitigates the limited dementia-relevant speech data problem by inheriting knowledge from similar but much larger datasets. Specifically, we built a variety of transfer learning models using commonly employed MobileNet (image), YAMNet (audio), Mockingjay (speech), and BERT (text) models. Results indicated that the transfer learning models of text data showed significantly better performance than those of audio data. Performance gains of the text models may be due to the high similarity between the pre-training text dataset and the CTP text dataset. Our multi-modal transfer learning introduced a slight improvement in accuracy, demonstrating that audio and text data provide limited complementary information. Multi-task transfer learning resulted in limited improvements in classification and a negative impact in regression. By analyzing the meaning behind the Alzheimer's disease (AD)/non-AD labels and Mini-Mental State Examination (MMSE) scores, we observed that the inconsistency between labels and scores could limit the performance of the multi-task learning, especially when the outputs of the single-task models are highly consistent with the corresponding labels/scores. In sum, we conducted a large comparative analysis of varying transfer learning models focusing less on model customization but more on pre-trained models and pre-training datasets. We revealed insightful relations among models, data types, and data labels in this research area.

In this study, we present WindTalker, a novel and practical keystroke inference framework that can be used to infer the sensitive keystrokes on a mobile device through WiFi-based side-channel information. WindTalker is motivated from an observation that keystrokes on mobile devices will lead to different hand coverage and the finger motions, which will introduce a unique interference to the multi-path signals and can be reflected by the channel state information (CSI). An attacker can exploit the strong correlation between the CSI fluctuation and the keystrokes to infer the user's password input. Compared with the previous keystroke inference approaches, WindTalker neither deploys external equipment physically close to the target device nor compromises the target device. Instead, it employs a more practical setting by deploying a free public WiFi hotspot and collects the CSI data from the target device as long as the device is connected to the hotspot. In addition, to improve inference accuracy and efficiency, it analyzes the WiFi traffic to selectively collect CSI only for the sensitive period where password entering occurs. WindTalker can be implemented without the requirement of visually seeing the target device, or installing any malware on the device. We tested Windtalker on several mobile phones and performed a detailed case study to evaluate the practicality of the password inference towards Alipay, the largest mobile payment platform in the world. Furthermore, we proposed a novel CSI obfuscation countermeasure to thwart the inference attack. The evaluation results show that the performance of WindTalker can be dramatically reduced by adopting the proposed countermeasures.

With the rapid growth of the health data scale, the limited storage and computation resources of wireless body area sensor networks (WBANs) is becoming a barrier to their development. Therefore, outsourcing the encrypted health data to the cloud has been an appealing strategy. However, date aggregation will become difficult. Some recently-proposed schemes try to address this problem. However, there are still some functions and privacy issues that are not discussed. In this paper, we propose a privacy-enhanced and multifunctional health data aggregation scheme (PMHA-DP) under differential privacy. Specifically, we achieve a new aggregation function, weighted average (WAAS), and design a privacy-enhanced aggregation scheme (PAAS) to protect the aggregated data from cloud servers. Besides, a histogram aggregation scheme with high accuracy is proposed. PMHA-DP supports fault tolerance while preserving data privacy. The performance evaluation shows that the proposal leads to less communication overhead than the existing one.