An energy trading system is essential for the successful integration of Electric Vehicles (EVs) into the smart grid. In this paper, leveraging blockchain technology, we first propose a privacy-preserving charging-station-to-vehicle (CS2V) energy trading scheme. The CS2V scheme is useful in crowded cities where there is a need for a charging infrastructure that can charge many EVs daily. We also propose a privacy-preserving vehicle-to-vehicle (V2V) energy trading scheme. The V2V scheme is useful when charging stations are not available or far and cheaper prices can be offered from EVs, e.g., if they charge from renewable energy sources. In the V2V scheme, the privacy of both charging and discharging EVs including location, time, and amount of power are preserved. To preserve privacy in both schemes, EVs are anonymous, however, a malicious EV may abuse the anonymity to launch Sybil attacks by pretending as multiple non-exiting EVs to launch powerful attacks such as Denial of Service (DoS) by submitting multiple reservations/offers without committing to them, to prevent other EVs from charging and make the trading system unreliable. To thwart the Sybil attacks, we use a common prefix linkable anonymous authentication scheme, so that if an EV submits multiple reservations/offers at the same timeslot, the blockchain can identify such submissions. To further protect the privacy of EV drivers, we introduce an anonymous and efficient blockchain-based payment system that cannot link individual drivers to specific charging locations. Our experimental results indicate that our schemes are secure and privacy-preserving with low communication and computation overheads.

Artificial Intelligence (AI) is changing every technology we are used to deal with. Autonomy has long been a sought-after goal in vehicles, and now more than ever we are very close to that goal. Big auto manufacturers as well are investing billions of dollars to produce Autonomous Vehicles (AVs). This new technology has the potential to provide more safety for passengers, less crowded roads, congestion alleviation, optimized traffic, fuel-saving, less pollution as well as enhanced travel experience among other benefits. But this new paradigm shift comes with newly introduced privacy issues and security concerns. Vehicles before were dumb mechanical devices, now they are becoming smart, computerized, and connected. They collect huge troves of information, which needs to be protected from breaches. In this work, we investigate security challenges and privacy concerns in AVs. We examine different attacks launched in a layer-based approach. We conceptualize the architecture of AVs in a four-layered model. Then, we survey security and privacy attacks and some of the most promising countermeasures to tackle them. Our goal is to shed light on the open research challenges in the area of AVs as well as offer directions for future research.

Alzheimer’s disease (AD) is a progressive illness with a slow start that lasts many years; the disease’s consequences are devastating to the patient and the patient’s family. If detected early, the disease’s impact and prognosis can be altered significantly. Blood biosamples are often employed in simple medical testing since they are cost-effective and easy to collect and analyze. This research provides a diagnostic model for Alzheimer’s disease based on federated learning (FL) and hardware acceleration using blood biosamples. We used blood biosample datasets provided by the ADNI website to compare and evaluate the performance of our models. FL has been used to train a shared model without sharing local devices’ raw data with a central server to preserve privacy. We developed a hardware acceleration approach for building our FL model so that we could speed up the training and testing procedures. The VHDL hardware description language and an Altera 10 GX FPGA are utilized to construct the hardware-accelerator approach. The results of the simulations reveal that the proposed methods achieve accuracy and sensitivity for early detection of 89% and 87%, respectively, while simultaneously requiring less time to train than other algorithms considered to be state-of-the-art. The proposed algorithms have a power consumption ranging from 35 to 39 mW, which qualifies them for use in limited devices. Furthermore, the result shows that the proposed method has a lower inference latency (61 ms) than the existing methods with fewer resources.

Hypertension is a chronic condition that is one of the prominent reasons behind cardiovascular disease, brain stroke, and organ failure. Left unnoticed and untreated, the deterioration in a health condition could even result in mortality. If it can be detected early, with proper treatment, undesirable outcomes can be avoided. Until now, the gold standard is the invasive way of measuring blood pressure (BP) using a catheter. Additionally, the cuff-based and noninvasive methods are too cumbersome or inconvenient for frequent measurement of BP. With the advancement of sensor technology, signal processing techniques, and machine learning algorithms, researchers are trying to find the perfect relationships between biomedical signals and changes in BP. This paper is a literature review of the studies conducted on the cuffless noninvasive measurement of BP using biomedical signals. Relevant articles were selected using specific criteria, then traditional techniques for BP measurement were discussed along with a motivation for cuffless measurement use of biomedical signals and machine learning algorithms. The review focused on the progression of different noninvasive cuffless techniques rather than comparing performance among different studies. The literature survey concluded that the use of deep learning proved to be the most accurate among all the cuffless measurement techniques. On the other side, this accuracy has several disadvantages, such as lack of interpretability, computationally extensive, standard validation protocol, and lack of collaboration with health professionals. Additionally, the continuing work by researchers is progressing with a potential solution for these challenges. Finally, future research directions have been provided to encounter the challenges.

Advanced Metering Infrastructure (AMI) networks allow utility companies to collect fine-grained power consumption data of electricity consumers for load monitoring and energy management. This brings serious privacy concerns since the fine-grained power consumption data can expose consumers’ activities. Privacy-preserving data aggregation techniques have been used to preserve consumers’ privacy while allowing the utility to obtain only the consumers total consumption. However, most of the existing schemes do not consider the multi-dimensional nature of power consumption in which electricity consumption can be categorized based on the consumption type. They also do not consider multi-subset data collection in which the utility should be able to obtain the number of consumers whose consumption lies within a specific consumption range, and the overall consumption of each set of consumers. In this paper, we propose an efficient and privacy-preserving multi-dimensional and multi-subset data collection scheme, named “MDMS”. In MDMS, the utility can obtain the total power consumption as well as the number of consumers of each subset in each dimension. In addition, for better scalability, MDMS allows the utility to delegate bill computation to the AMI networks’ gateways using the encrypted readings and following dynamic prices in which electricity prices are different based on both the time and the consumption type. Moreover, MDMS uses lightweight operations in encryption, aggregation, and decryption resulting in low computation and communication overheads as given in our experimental results. Our security analysis demonstrates that MDMS is secure and can resist collusion attacks that aim to reveal the consumers’ readings.

Charging coordination is necessary for the successful integration of the Energy Storage Units (ESUs), including electric vehicles and home batteries, into the smart grid. To coordinate charging, the ESUs should send charging requests including time-to-complete-charging (TCC) and battery state-of-charge (SoC) to the charging controller (CC) for scheduling charging, but these data can reveal sensitive information on the ESUs’ owners such as their locations, when they return home and whether they are on travel. In this article, we propose centralized and decentralized privacy-preserving and collusion-resistant charging coordination schemes for ESUs. In the centralized scheme, ESUs authenticate their requests using anonymous tokens. To thwart linkability attacks where the CC uses TCC and SoC to link requests sent from the same ESU at consecutive time slots, an ESU needs to send multiple charging requests with different TCC and SoC values instead of only one request. In the decentralized scheme, charging is coordinated in a distributed way using a privacy-preserving data aggregation technique. The idea is that each ESU selects some ESUs to act as proxies, and shares a secret mask with each proxy. Then, each ESU adds a mask to its charging request and encrypts it so that by aggregating all requests, all masks are nullified and the total charging demand is known, and then it is used to compute the charging schedules. Due to using masking technique, the scheme is secure against collusion attacks. The results of extensive experiments and simulations confirm that our schemes are efficient and secure, and can preserve ESU owners’ privacy and thwart linkability attacks.