With advancements in Internet of Things (IoT) technologies for Intelligent Transportation Systems (ITS), gathered vehicle data can provide insights into emerging vehicular phenomena and help the continued enhancement of creative and efficient vehicular systems. Overall, improvements to ITS have had a significant influence on society. Predictive maintenance will discover faults within the vehicle and offer early warnings to avert failure by using data collected from car sensors and maintenance models built from prior vehicle repairs. The primary goal of this study is to develop a secure, privacy-preserving, and continuous data-gathering strategy for predictive maintenance, utilizing a K-Nearest Neighbor (KNN) aggregation over an encrypted data scheme and a Neural Network (NN) prediction model. In this suggested approach, data will be exchanged among vehicles, fog nodes, and a cloud server. The vehicle’s sensors will produce a sensory data report and transmit it to the fog nodes after encryption to preserve the vehicle’s user privacy. The encrypted information will be aggregated through the fog nodes before being transferred to the cloud server. Finally, the cloud server will issue maintenance details to fog nodes and vehicles using an NN predictive model. Furthermore, the proposed scheme’s capability for implementation on hardware is comprehensively examined and evaluated. Our security and privacy evaluation shows that the scheme can achieve our design goals. Additionally, our performance evaluation shows that our scheme has low computation and communication overheads compared with the existing techniques. Especially for the NN-based model that achieves 100% accuracy, 100% precision, 97.5% recall, and 98.7% F1-score through the software tests; at the same time, it performs with good utilization values on the FPGA board.

Drunk driving detection systems are traditionally reactive, relying on tools such as breathalyzers or field sobriety tests deployed only after unsafe behavior is observed. To address this limitation, recent advances in computer vision and deep learning have enabled the development of proactive, non-intrusive systems capable of detecting intoxication based on facial features. This paper expands on our previous work by focusing on out-of-vehicle drunk driving detection, leveraging facial imagery captured from external sources such as roadside cameras or drones. We apply Machine Learning (ML) and Deep Learning (DL) models to a large-scale dataset of sober and intoxicated faces, introducing controlled salt-and-pepper noise at 20%, 40%, and 50% levels, along with disruption techniques such as flipping and brightness variations to simulate real-world surveillance conditions. We proposed two configuration modes, low-resource and high-resource models, to illustrate the applicability of our scheme to devices with different resource constraints. To enhance transparency, we integrate Explainable AI (XAI) tools—such as saliency maps—to identify key facial regions influencing model decisions. In addition to software-based evaluation, this work investigates the feasibility of real-time deployment through a hardware–software co-design implemented on a Field-Programmable Gate Array (FPGA) platform. Both low-resource and high-resource model configurations are analyzed with respect to architectural design and resource utilization, demonstrating the practicality of embedded inference in roadside and edge environments. By supporting efficient edge-level inference, the proposed system is well-suited for Internet-of-Things (IoT) deployments that rely on distributed roadside sensors and embedded processing platforms.

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.

Ridesharing allows multiple persons to share one vehicle for their trips instead of using multiple vehicles. Ridesharing can reduce the number of vehicles in the street, which consequently can reduce air pollution, traffic congestion, and transportation cost. However, ridesharing organization requires passengers to report sensitive location information about their trips to a trip organizing server (TOS) which creates a serious privacy issue. The existing ridesharing organization schemes are neither flexible nor scalable in the sense that they require a driver and a rider to have exactly the same trip to share a ride, and they are inefficient if applied to large geographic areas. In this paper, we propose two efficient privacy-preserving ridesharing organization schemes for Non-transferable Ridesharing Service (NRS) and Transferable Ridesharing Service (TRS). In NRS, a rider shares a ride from his/her trip's start to the destination with only one driver, whereas, in TRS, a rider can transfer between multiple drivers while en route until he reaches his destination. In the proposed schemes, the ridesharing area is divided into a number of small geographic areas, called cells, and each cell has a unique identifier. Each driver/rider should encrypt his/her trip's data with modified kNN encryption scheme, and send an encrypted ridesharing offer/request to the TOS. In NRS scheme, Bloom filters are used to represent the trip information compactly before encryption. Then, the TOS can measure the similarity of the encrypted trips to organize shared rides without revealing either the users' identities or the locations. In TRS scheme, drivers report their encrypted routes, and then the TOS builds a directed graph that is passed to a modified version of Dijkstra's shortest path algorithm to search for an optimal path for rides that can achieve a set of preferences prescribed by the riders. Although TRS can be used to organize non-transferable trips, performance evaluation shows that NRS requires less communication overhead than TRS. Our formal privacy proof and analysis demonstrate that the proposed schemes can preserve users privacy and our experimental results using routes extracted from real maps show that the proposed schemes can be used efficiently for large cities.

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 multidimensional nature of power consumption in which electricity consumption can be categorized based on the consumption type. They also do not consider multisubset 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 article, we propose an efficient and privacy-preserving multidimensional and multisubset 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 the 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.

In this paper, we propose privacy-preserving fine-grained data retrieval schemes for mobile social networks (MSNs). The schemes enable users to retrieve data from other users who are interested in some topics related to a subject of interest. We define a subject to be a broad term that can cover many fine-grained topics, e.g., History can be a subject and World War I can be a topic. We consider centralized and decentralized network models. Our centralized scheme allows users to securely outsource data to a server such that the server matches the users who are interested in same topic(s) and have defined social attributes with privacy preservation. Searchable encryption scheme and a proposed cryptography construct are used to enable the server to match the topics and attributes without knowing any private information. By using the social attributes, users can prescribe the other users who can be connected to. We also propose a decentralized scheme that can be used when there is no connection to the server, i.e, shortage of Internet connectivity. The scheme leverages friends-of-friends relationship and transferable trust concept, where each user trusts his friends and the friends of friends. If a friend is not interested in the requested subject, he/she can link him/her to his/her friends without knowing the requested subject to preserve privacy. Our schemes use Bloom filters to store the topics of interest to reduce the storage and communication overhead. This is important because the number of fine-grained topics can be large. Different techniques to store the topics in the filter are proposed and investigated. Performance metrics are proposed and evaluated using real implementations. Our analysis and implementation results demonstrate that our schemes can preserve the privacy of the MSN users with high performance.

The availability of high-performance computing platforms, large storage devices, and high- speed communications have boosted the popularity of cloud computing. Users exploit these capabilities by using the cloud as a repository for their data and sharing these data with others. However, since the cloud is usually owned and operated by private companies, storing sensitive data in the cloud servers raises privacy concerns. To address these concerns, privacy-preserving keyword search schemes have been developed. Nevertheless, most of the existing schemes are either inefficient for multi-data- owner settings or designed for single-data-owner settings, and becomes insecure and inefficient when used for multi-data-owner. This paper proposes an efficient multi-keyword ranked search scheme over encrypted data for multi-data-owner settings. The proposed scheme allows each data owner and each user to have a distinct key, and allows the server to efficiently search the files of different data owners using one encrypted query sent by the user. Our privacy analysis demonstrates that the proposed scheme can preserve the privacy of the data owners and users. In addition, our extensive performance evaluations demonstrate that our scheme is much more efficient than existing approaches in the literature.

The dynamic charging technology will enable Electric Vehicles (EVs) to charge their batteries while moving. Special charging pads will be placed on the roads to charge the EVs through the magnetic induction. The dynamic charging system should communicate with the EVs to only charge authorized vehicles and ensure payment integrity. This communication should be secured and should not leak any private information of the EV drivers, especially location information. In this paper, we propose an efficient scheme to secure the dynamic charging system and preserve the privacy of the drivers. The scheme uses a combination of different cryptosystems to achieve security and privacy. Anonymous coins are used to ensure anonymous payment and authentication. We also developed a hierarchical authentication scheme that uses efficient cryptosystems like hashing and Exclusive-OR operations. In addition, the proposed scheme considers the characteristics of the dynamic charging system such as the large number of pads having limited computational resources and the short contact time between EVs and pads due to the high speed of EVs. Our analysis demonstrates that the proposed scheme is secure and can preserve privacy. In addition, our measurements confirm that the proposed scheme is efficient.

In Long Term Evolution-Advanced (LTE-A) networks, Mobile Relay Nodes (MRNs) are installed in fast moving buses and trains to connect the passengers' devices to evolved Node B (eNB). However, since the MRNs and eNBs are installed in open environment, they can be compromised to launch security and privacy attacks. In this paper, we propose a privacy preserving intra Mobility Management Entity (MME) group handover scheme in LTE-A networks for repeated trips. Comparing to the existing schemes, the proposed scheme is devised to achieve the following requirements. First, the MRNs should be able to authenticate the received messages so that the messages sent from external attackers can be dropped by the MRNs rather than forwarding them to the core network. Second, the proposed scheme also aims to reduce the computational and signaling overhead and establish secure session keys. Third, the scheme aims to prevent MRNs and eNBs from tracking passengers' locations especially if they take same trip regularly. Our analysis demonstrates that the proposed scheme can achieve our security and privacy objectives. Our performance evaluations demonstrate that the proposed scheme requires a few number of messages and low computation overhead.

The autonomous vehicles will make ride sharing popular, and necessary. However, ride sharing organization requires the passengers to reveal sensitive information about their trips, which causes a serious privacy issue. In this paper, we propose a privacy-preserving ride sharing organization scheme using the kNN encryption scheme, Bloom filter, and group signature. Each user encrypts his trip's data and sends an encrypted ride-sharing request to a server that measures the similarity between users trips' to organize shared rides without revealing sensitive information. Comparing to our proposal in [1], this paper has three improvements. The proposed scheme is much more efficient because the trip data is much shorter. It is also more secure because each user has his own encryption key instead of using one shared key for all users. It can prevent linking the encryptions of the trip's data sent at different times because users frequently update their keys efficiently. Our privacy analysis demonstrates that the proposed scheme can preserve users' location privacy and trips' data privacy. Our experimental results on a real map demonstrate that the proposed scheme is much more efficient than the existing schemes, especially for large cities.

Ride sharing can reduce the number of vehicles in the streets by increasing the occupancy of vehicles, which can facilitate traffic and reduce crashes and the number of needed parking slots. Autonomous vehicles can make ride sharing convenient, popular, and also necessary because of the elimination of the driver effort and the expected high cost of the vehicles. However, the organization of ride sharing requires the users to disclose sensitive detailed information not only on the pick-up/drop-off locations but also on the trip time and route. In this paper, we propose a scheme to organize ride sharing and address the unique privacy issues. Our scheme uses a similarity measurement technique over encrypted data to preserve the privacy of trip data. The ride sharing region is divided into cells and each cell is represented by one bit in a binary vector. Each user should represent trip data as binary vectors and submit the encryptions of the vectors to a server. The server can measure the similarity of the users' trip data and find users who can share rides without knowing the data. Our analysis has demonstrated that the proposed scheme can organize ride sharing without disclosing private information. We have implemented our scheme using Visual C on a real map and the measurements have confirmed that our scheme is effective when ride sharing becomes popular and the server needs to organize a large number of rides in short time.