Machine learning (ML) anomaly detectors are commonly used to identify cyber-attacks on smart power grids because they can detect new (i.e., zero-day) attacks by classifying deviations from normal patterns as anomalies. Deeplearning-based anomaly detectors offer superior performance but are highly sensitive to the selection of threshold values for defining anomalies. Conversely, traditional (or shallow-based) detectors avoid this threshold sensitivity but often underperform, particularly when dealing with complex interdependent data. Moreover, like all ML models, these detectors are vulnerable to adversarial evasion attacks, where adversaries make small and subtle manipulations to false data to evade detection. To address these issues, we propose a robust hybrid-based anomaly detector that combines the strengths of both deep and shallow-based and is trained using explanations derived from power consumption readings rather than the raw readings themselves. This hybrid approach not only mitigates threshold sensitivity and improves performance but also enhances robustness against white-box evasion attacks. Additionally, we introduce an interpretability method using occlusion sensitivity, which helps explain how a classification decision is made for an input power consumption sample, thereby increasing trust, reliability, and understanding of various attack patterns.

Existing literature confirms the ability of machine learning to identify fraudulent smart grid power consumers who report false consumption readings to pay less electricity bills. Additionally, federated learning (FL) shows promise as a way to train the detection model without requiring data sharing, thereby safeguarding consumer privacy. However, malicious participants (i.e., clients) in FL training can launch adversarial attacks by training their local models with specially crafted low-consumption data to inject a Trojan into the global model. This Trojan can then be activated during the evaluation phase to evade the detection of false data. To the best of our knowledge, not enough research has been done on this topic in the context of unsupervised learning. The absence of labels in unsupervised learning exacerbates the effectiveness of Trojan attacks and renders it more challenging to design robust defense mechanisms. In this article, we first investigate the vulnerability of one-class classifiers to Trojan attacks. Then, we propose two defense approaches named layerwise close-to-median (LWCM) and Machine Unlearning to counter this attack. In LWCM, by choosing a FL client whose last layer model parameters are near to the median of all clients’ last layer parameters to update the global model, we can identify and exclude malicious updates. The idea is that the last layer parameters of honest clients should be similar, whereas those from malicious clients are different. With the majority of clients being honest, the median values are closer to the parameters of these clients, facilitating the detection of malicious clients. In Machine Unlearning, we utilize gradient ascent-based techniques to adapt models by selectively removing attacker-related data points. This is possible because honest clients generate data resembling that of malicious clients and employ a dual-component loss function to maintain model proficiency in recognizing benign power consumption patterns while eliminating malicious patterns. To show the seriousness of Trojan attacks and the effectiveness of our countermeasures, many experiments have been carried out.

In smart grids, smart meters (SMs) transmit power consumption data to utilities for billing and energy management. However, compromised SMs can report low consumption to reduce electricity bills. Deep reinforcement learning (DRL) detectors have recently been proposed to detect these attacks due to their adaptability to new attacks and changes in power consumption patterns. This article explores backdoor attacks targeting DRL detectors during training, aiming to introduce a vulnerability in the detector. These attacks make the detector misclassify false low-consumption data when trigger samples are used while maintaining normal classification accuracy otherwise. We propose a DRL-based attack model that generates stealthy and unique trigger samples using cosine similarity. Our evaluations show the attack is initially highly successful, but its success diminishes with honest data used for incremental training of the detector. To sustain high success rates, attackers must influence incremental training. We also propose defenses, including data filtration during the preparation stage, adversarial training for the defense model during the training stage, and a combined approach, with experiments validating their effectiveness.

Federated Learning (FL) enables decentralized training of machine learning (ML) models, making it a valuable approach for detecting false data in smart power grids (SGs) to enhance grid stability while protecting consumers privacy. However, FL-based ML models remain vulnerable to adversarial attacks during both training and inference phases, which can compromise data security. To address these vulnerabilities, we first investigate the robustness of a novel FL-based false data detection approach using Explainable Artificial Intelligence (XAI), referred to as XAI-based FL detection. This approach utilizes explanations of consumers power consumption data, rather than raw data, during the training process. We assess the robustness of the XAI-based FL detection compared to traditional data-driven FL detection against two types of adversarial attacks: Gradient Inversion attacks in the training phase, where adversaries reconstruct private data from shared gradients, and Evasion attacks in the inference phase, where adversaries subtly modify input data to deceive the detection model. Then, we propose a secure XAI-based FL detector with adversarial training to defend against both attack types. The key idea is that XAI helps mask model gradients during training because XAI-generated explanations remain nearly identical across different samples. Therefore, attackers struggle to accurately reconstruct the original training data, even if they obtain precise explanations using gradient inversion attacks. Additionally, XAI effectively distinguishes between benign and malicious samples. When combined with adversarial training, XAI strengthens model robustness against evasion attacks without compromising accuracy, effectively resolving the trade-off between security and performance. Our proposed detector reduced the success rate of evasion attacks from 94.99% to 29.11 explanations, and further to 0% with adding adversarial training. It also increased the mean square error for gradient inversion attacks from 0.01 to 2.60 in the most severe attack scenarios, making such attacks ineffective.

In the advanced metering infrastructure (AMI) of the smart grid, smart meters (SMs) are deployed to collect fine-grained electricity consumption data, enabling billing, load monitoring, and efficient energy management. However, some consumers engage in fraudulent behavior by hacking their meters, leading to either traditional electricity theft or more sophisticated evasion attacks. Evasion attacks aim to illegally reduce electricity bills while deceiving theft detection mechanisms. The current methods for identifying such attacks raise privacy concerns due to the need for access to consumers' detailed consumption data to train detection mechanisms. To address privacy concerns, federated learning (FL) is proposed as a collaborative training approach across multiple consumers. Adversarial training (AT) has shown promise in countering evasion threats on machine learning models. This paper, first, investigates the susceptibility of traditional electricity theft classifiers trained by FL to evasion attacks for both independent and identically distributed (IID) and Non-IID consumption data. Then, it investigates the effectiveness of AT in securing the global electricity theft detector against evasion attacks, assuming no misbehavior from the participant consumers in the FL process. After that, we introduce a novel attack, called Distillation, which can be launched during the AT process to make the global model susceptible to evasion at inference time. Finally, extensive experiments are conducted to validate the severity of the proposed attack.

In smart power grids, certain customers are motivated by financial gains to manipulate electricity consumption data, aiming to reduce their bills. Despite the development of machine learning-based detectors, these systems remain vulnerable to evasion attacks. This paper investigates the susceptibility of deep reinforcement learning (DRL)-based detectors to evasion attacks. We propose an evasion attack model that employs the double deep Q learning (DDQN) algorithm for a black-box attack scenario. Our model generates adversarial evasion samples by altering malicious consumption data, tricking detectors into classifying them as benign. Leveraging the unique attributes of reinforcement learning (RL), our model determines optimal actions for manipulating malicious data. For comparative analysis, we compare our DRL-based model with an FGSM-based attack model. Our experiments consistently demonstrate the effectiveness of our DRL-based attack model, achieving an impressive attack success rate (ASR) ranging from 92.92% to 99.96%, outperforming the FGSM-based attack model.

In smart power grids, electricity theft causes huge economic losses to electrical utility companies. Machine learning (ML), especially deep neural network (DNN) models hold state-of-the-art performance in detecting electricity theft cyberattacks. However, DNN models are vulnerable to adversarial attacks, i.e., evasion attacks. In this work, we study the vulnerability of the DNN-based electricity theft detectors against evasion attacks and the influence of the model's regularization (generalization) on robustness. We cluster the power consumers and train a detector for each cluster, and compare the performance and robustness of this detector to a global detector that is trained on all the consumers, data. The results indicate that the cluster-based detector is not only more robust against evasion attacks but also enhances normal classification accuracy because its training data has more consumption pattern similarity compared to the training data of the global detector which requires higher level of regularization. Moreover, unlike the existing solutions that sacrifice the normal accuracy of the model to improve the robustness against evasion attacks, the proposed cluster-based detector holds state-of-the-art performance in both robustness and accuracy.

The utilization of Advanced Metering Infrastructure (AMI) technology is for recording and billing customers for electricity consumption. This technology is vulnerable to cyber-attacks where customers under report their electricity usage, causing financial losses for electricity providers. Machine learning (ML) can be used to detect electricity theft, but it is challenging due to the absence of malicious data. To address this challenge, most of the existing works proposed specific attacks, however, these works are only effective on the proposed attacks and fail on new attacks. Some works proposed using anomaly detectors trained only on begin dataset. However, they rely on specific attacks to set classification thresholds, leading to failure in detecting zero-day attacks. Therefore, this paper proposes a one-class classification approach for electricity theft detection depending only on benign data and without assuming any attacks. First, the paper re-evaluates an existing detector that sets a reconstruction error threshold. Then, it proposes a detector combining decisions from three one-class ML models, including a one-class support vector machine (OC-SVM) trained on benign data, an OC-SVM trained on the bottleneck outputs of an autoencoder trained only on benign data, an OC-SVM trained on the mean squared errors of the reconstructed data of the autoencoder. The evaluation results confirm the superiority of the proposed detector over its individual components and the existing detectors.

In the realm of smart grids, smart meters can be hacked to report false data to lower the consumers’ electricity bills. While machine learning (ML) techniques have shown promise in detecting false data, they are also prone to adversarial attacks, such as evasion attacks. This article investigates the impact of gradient-ensemble-based evasion attacks on the smart grid ML-based false data detectors, focusing on the white-box threat model where attackers possess detailed knowledge of the defense mechanism. First, we examines the vulnerability of three detectors (consumer-based, cluster-based, and global) to gradient-based evasion attacks. The evaluation results show an inverse relationship between robustness of the detectors and regularization (i.e., generalization), where higher data set variability usually causes higher regularization. Notably, minimal regularization level is observed when electricity consumption patterns are close. Our findings also indicate that the consumer-based detector exhibits higher accuracy and robustness but remains susceptible to zero day attacks and demands substantial computational resources for training an ML model for each consumer. In contrast, the cluster-based detector improves accuracy and exhibits satisfactory robustness compared to the global detector. Subsequently, we proposes two parallel-ensemble approaches (stacking and voting) for the cluster-based false data detectors trained on the adversarial samples. The evaluation results demonstrate that integrating clustering, adversarial training, and ensemble methods, the proposed detector enhances robustness against gradient-ensemble-based evasion attacks while significantly boosting accuracy. This stands in contrast to benchmark defenses, which often face a tradeoff between accuracy and robustness, sacrificing accuracy to bolster resilience against evasion attacks.

With the rapid advances in mobile and communication technologies, mobile devices are equipped with powerful processors, various sensors, large memories, and fast wireless communication modules. By taking advantage of powerful mobile devices and human intelligence, mobile crowdsourcing is an emerging paradigm that enables users to outsource tasks (usually difficult to accomplish individually) to a group of people (workers) at an affordable price. Specifically, human mobility offers unprecedented opportunities to sense the surroundings wherever their holders arrive, and human capabilities also offer intelligent human-assisted computation with their devices, e.g., human perception, intelligence, cognition, knowledge, visual recognition, and experiences.

As the number of electric vehicles on roads significantly increases, spatial-temporal charging coordination mechanisms have been introduced for balancing charging demand and energy supply. However, electric vehicles could send false data, such as state-of-charge (SoC), to the charging coordination mechanism for gaining high charging priority illegally. Machine Learning models can be used to detect false data. However, in our application the detector is trained on a dataset that contains sensitive information, such as the locations and SoC values of the electric vehicles. Therefore, attackers could launch adversarial attacks against the detector, such as membership inference and model inversion, for revealing sensitive information on the drivers whose data are used to train the detector. Furthermore, attackers could launch evasion attacks against the detector by computing false SoC values that are classified benign by the detector. Addressing the three attacks simultaneously makes the problem more complicated because a countermeasure to one attack may degrade the model's accuracy and unintentionally make the model more susceptible to other attacks. Accordingly, in this paper, we propose a deep-learning training approach for false data detector in spatial-temporal charging coordination. Our approach can deal with the tradeoffs and balance the detector's accuracy and robustness against the adversarial attacks. Specifically, our approach combines three techniques, including mimic learning, dropout, and differential privacy, in a certain way that makes the detector highly accurate in detecting false data and also robust against adversarial attacks. To validate our approach, we have conducted a set of experiments and the given results demonstrate the robustness and accuracy of our detector.

Decision tree (DT) models are widely used in medical applications where the size of the data sets is usually small or medium. Moreover, DT ensemble models are preferred over single DT models because of their higher accuracy in spite of the need for more overhead due to using multiple trees. Several schemes have been proposed for privacy-preserving cloud-based medical diagnosis using ensemble models. However, these schemes suffer from several limitations. First, they suffer from high computation/communication overheads due to using inefficient public-key cryptosystems. Second, none of them can simultaneously protect the intellectual property of the model and preserve the privacy of the patients’ data and diagnosis results. Finally, they do not provide inherent access control for the outsourced model and micropayment, in which only the registered patients can use the model and pay for the service. In this article, we develop a lightweight and privacy-preserving cloud-based medical diagnosis scheme using ensemble models with high accuracy and acceptable overhead. Using our scheme, the model owner can control the patients who can use the model. Also, for each classification operation, patients must make a micro-payment to pay for the diagnosis service. Our analysis indicates that our scheme can protect the model’s intellectual property and diagnose diseases without leaking any sensitive information about the patients’ medical data and the diagnosis results. Our experimental results demonstrate that our scheme requires less communication/computation overhead compared to the existing schemes.

The smart grid advanced metering infrastructure (AMI) is vulnerable to electricity theft cyber-attacks in which malicious smart meters report low readings to reduce the consumers’ bills. To avoid this problem, several machine-learning-based detectors have been proposed to detect electricity theft. Most of these detectors are global in the sense that they are trained on different consumption levels, including low and high consumptions, to be used for all consumers. In this article, we introduce a novel type of evasion attacks against global detectors as follows. A malicious consumer who has high consumption level can send false readings for a low-consumption profile (that resembles the profiles the detector is trained on) to evade the detector, i.e., steal electricity without being detected. We first conduct experiments to prove that the existing global detectors are vulnerable to this new kind of evasion attacks. To launch this attack, we train a generative adversarial network (GAN) on a real data set to generate fake low-consumption readings that can evade the detector. The given results indicate that the success rate of the attack is between 82% and 97%. To thwart this attack, we divide the consumers into clusters of close electricity consumption levels and train one detector for each cluster. Therefore, if a malicious consumer in any cluster tries to imitate the consumption profiles of consumers in other clusters, he/she will be detected. On the other hand, it is not profitable to imitate the electricity consumption profiles of consumers in his/her cluster to evade detection. To prove the effectiveness of our countermeasure, extensive experiments are conducted and the results indicate that our countermeasure can successfully thwart the attack.

Energy forecasting is important because it enables infrastructure planning and power dispatching while reducing power outages and equipment failures. It is well-known that federated learning (FL) can be used to build a global energy predictor for smart grids without revealing the customers’ raw data to preserve privacy. However, it still reveals local models’ parameters during the training process, which may still leak customers’ data privacy. In addition, for the global model to converge, it requires multiple training rounds, which must be done in a communication-efficient way. Moreover, most existing works only focus on load forecasting while neglecting energy forecasting in net-metering systems. To address these limitations, in this article, we propose a privacy-preserving and communication-efficient FL-based energy predictor for net-metering systems. Based on a data set for real power consumption/generation readings, we first propose a multidata-source hybrid deep learning (DL)-based predictor to accurately predict future readings. Then, we repurpose an efficient inner-product functional encryption (IPFE) scheme for implementing secure data aggregation to preserve the customers’ privacy by encrypting their models’ parameters during the FL training. To address communication efficiency, we use a change and transmit (CAT) approach to update local model’s parameters, where only the parameters with sufficient changes are updated. Our extensive studies demonstrate that our approach accurately predicts future readings while providing privacy protection and high communication efficiency.

Energy theft causes economic losses and power out-ages and disrupts energy generation and distribution of smart grids. A significant challenge is how to effectively use customers' power consumption data for energy theft detection while pre-serving security and privacy. One solution is to use federated learning (FL) to compute a global model to detect energy theft cyberattacks where detection stations train local models on their customers' power consumption data and send only the parameters of the models to an aggregator server. Nevertheless, revealing the model's parameters may still leak customers' private data by launching attacks such as membership and inference. Therefore, a secure aggregation scheme is needed to protect the models' param-eters. Furthermore, the existing privacy-preserving aggregation schemes suffer from high overhead and low model accuracy. This paper addresses these limitations by proposing a novel privacy- preserving, efficient, decentralized, aggregation scheme based on a functional encryption cryptosystem for energy theft detection in smart grids without requiring a key distribution center. Our scheme enables the detection stations to send encrypted training parameters to an aggregator, which calculates the aggregated parameters and returns the updated model parameters to the detection stations without being able to learn the parameters of the local models or the training data of the customers to preserve their privacy. Moreover, the results of our extensive experiments show that our FL-based detector can detect energy thefts accurately with low overhead because of our lightweight privacy-preserving aggregation scheme.

General Purpose Vision System (GPVS) is a task-agnostic vision-language system that inputs an image and a question from which the system recognizes the tasks to be performed and outputs bounding boxes, confidence scores, and text outputs to answer the question. While much attention to GPVS has been recently given in the computer vision field, its medical field applications are still in their infancy. This paper presents MED-GPVS, a customized deep learning-based GPVS on biomedical images to perform various vision tasks, such as object detection and visual question answering, on medical images to facilitate precision medicine/e-health services. Our envisioned MED-GPVS takes an image and a natural language text as inputs, and then outputs bounding boxes, confidence scores, and generates a caption (i.e., the answer to the posed query). For example, if a medical image of a patient’s abdomen is presented to MED-GPVS followed by the question: "does the picture contain stomach?", MED-GPVS should ideally provide the answer "yes" along with a prediction box and prediction score on the image. We utilize the multilingual SLAKE dataset, which was annotated by expert physicians with a full semantic label, to validate the performance of MED-GPVS under various scenarios involving different biomedical image-based diagnoses. For the visual question answering (VQA) task, MED-GPVS demonstrates encouraging performance with significantly high accuracy of 82.41%.

Federated learning (FL) has emerged to leverage datasets from multiple devices to improve the performance of a machine learning (ML) model while providing privacy preservation for devices. The training data is collected at the devices, also known as FL workers, which collaboratively train a global learning model and share their local model updates with a central entity or server without sharing their data. However, FL can be susceptible to various adversarial attacks that target its security and privacy. In particular, the workers can upload unreliable local model updates, leading to corruption of the main FL task. Workers may intentionally contribute unreliable local updates by launching poisoning attacks or unintentionally by updating low-quality models caused by high device mobility, limited device resources, or unstable network connection. Consequently, identifying reliable and trustworthy workers becomes critical for FL security. In this article, the concept of reputation is adopted as a metric to evaluate workers’ reliability and trustworthiness. In addition, deep reinforcement learning (DRL)-based reputation mechanism is proposed for optimal selection and evaluation of reliable FL workers. Due to the dynamic nature of worker behavior in the FL environment, the DRL-based algorithm deep deterministic policy gradient (DDPG) is employed to improve the FL model accuracy and stability. We compare the performance of our proposed method with a conventional reputation method and deep$Q$-networks (DQNs)-based reputation method. Our simulation results demonstrate that our proposed method can improve FL accuracy by more than 30% under various scenarios and achieves better convergence than the other methods.

Contact tracing is a very effective way to control the COVID-19-like pandemics. It aims to identify individuals who closely contacted an infected person during the incubation period of the virus and notify them to quarantine. However, the existing systems suffer from privacy, security, and efficiency issues. To address these limitations, in this article, we propose an efficient and privacy-preserving Blockchain-based infection control system. Instead of depending on a single authority to run the system, a group of health authorities, that form a consortium Blockchain, run our system. Using Blockchain technology not only secures our system against single point of failure and denial of service attacks, but also brings transparency because all transactions can be validated by different parties. Although contact tracing is important, it is not enough to effectively control an infection. Thus, unlike most of the existing systems that focus only on contact tracing, our system consists of three integrated subsystems, including contact tracing, public places access control, and safe-places recommendation. The access control subsystem prevents infected people from visiting public places to prevent spreading the virus, and the recommendation subsystem categorizes zones based on the infection level so that people can avoid visiting contaminated zones. Our analysis demonstrates that our system is secure and preserves the privacy of the users against identification, social graph disclosure, and tracking attacks, while thwarting false reporting (or panic) attacks. Moreover, our extensive performance evaluations demonstrate the scalability of our system (which is desirable in pandemics) due to its low communication, computation, and storage overheads.

In the smart grid, malicious customers may compromise their smart meters (SMs) to report false readings to achieve financial gains illegally. This causes hefty financial losses to the utility and may degrade the grid performance because the reported readings are used for energy management. This article is the first work that investigates this problem in the net-metering system, in which one SM is used to report the difference between the power consumed and the power generated. First, we prepare a benign data set for the net-metering system by processing a real power consumption and generation data set. Then, we propose a new set of attacks tailored for the net-metering system to create a malicious data set. After that, we analyzed the data and found time correlations between the net meter readings and correlations between the readings and relevant data obtained from trustworthy sources, such as solar irradiance and temperature. Based on the data analysis, we propose a general multidata-source deep hybrid learning-based detector to identify the false-reading attacks. Our detector is trained on net meter readings of all customers besides data from trustworthy sources to enhance the detector performance by learning the correlations between them. The rationale here is that although an attacker can report false readings, he cannot manipulate the solar irradiance and temperature values because they are beyond his control. Extensive experiments have been conducted, and the results indicate that our detector can identify the false-reading attacks with a high detection rate of 98.59% and a low false alarm of 2.92%.

The periodic transmission of the customers’ power consumption readings in the advanced metering infrastructure (AMI) is essential for energy management and billing. To collect the readings efficiently, the change and transmit approach is adopted in AMI (CAT AMI) so that the readings are reported only when there is enough change in the consumption. However, CAT AMI suffers from malicious customers who launch electricity-theft cyberattacks by manipulating their readings to illegally reduce their bills. These attacks can cause hefty financial losses and degrade the grid performance because the readings are used for grid management. In this article, the electricity-theft problem in CAT AMI networks is investigated. We first process a real power consumption readings data set to create a benign data set and propose a new set of cyberattacks to create malicious samples. We then develop a deep-learning-based electricity-theft detection solution to identify malicious customers for the CAT AMI network. The proposed detector uses both the customers’ transmission pattern and CAT readings to learn the correlation between them in order to enhance the detector’s ability in identifying electricity thefts. We conduct extensive experiments to evaluate the performance of our electricity-theft detector, and the results indicate that our detector can accurately detect malicious customers and achieve higher detection rate and lower false alarm than the detectors that are trained only on the CAT readings.

Vehicular Ad Hoc Networks (VANETs) have the potential to enable the next-generation Intelligent Transportation Systems (ITS). In ITS, data contributed by vehicles can build a spatio-temporal view of traffic statistics, which can improve road safety and reduce slow traffic and jams. To preserve drivers’ privacy, vehicles should use multiple pseudonyms instead of only one identity. However, vehicles may exploit this abundance of pseudonyms and launch Sybil attacks by pretending to be multiple vehicles. Then, these Sybil (or fake) vehicles report false data, e.g., to create fake congestion or pollute traffic management data. In this article, we propose a Sybil attack detection scheme using proofs of work and location. The idea is that each road side unit (RSU) issues a signed time-stamped tag as a proof for the vehicle’s anonymous location. Proofs sent from multiple consecutive RSUs are used to create a trajectory which is used as vehicle anonymous identity. Also, contributions from one RSU are not enough to create trajectories, rather the contributions of several RSUs are needed. By this way, attackers need to compromise an infeasible number of RSUs to create fake trajectories. Moreover, upon receiving the proof of location from an RSU, the vehicle should solve a computational puzzle by running proof of work (PoW) algorithm. Then, it should provide a valid solution (proof of work) to the next RSU before it can obtain a proof of location. Using the PoW can prevent the vehicles from creating multiple trajectories in case of low-dense RSUs. To report an event, the vehicle has to send the latest trajectory to an event manager. Then, the event manager uses a matching technique to identify the trajectories sent from Sybil vehicles. The scheme depends on the fact that the Sybil trajectories are bounded physically to one vehicle, and therefore, their trajectories should overlap. Extensive experiments and simulations demonstrate that our scheme achieves high detection rate of Sybil attacks with low false negative and acceptable communication and computation overhead.

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.

An energy-trading system is essential for the successful integration of Electric vehicles (EVs) into the smart grid. Existing systems merely focus on making optimal decisions while others depend on anonymization to achieve EVs drivers' privacy which is not enough because they can be identified from visited locations. In this paper, leveraging blockchain technology, we propose a privacy-preserving charging-station-to-vehicle (CS2V) energy trading scheme. To preserve privacy, 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.

To outsource medical data to the cloud, several schemes have been recently proposed to enable search over encrypted data to preserve data owners’ privacy. However, most of the existing schemes are either inefficient as they suffer from high computation/communication overheads or they are designed only to support single-data-owner and they unfortunately do not take in consideration the multi-data-owner setting of the e-health applications. In medical applications, a user, e.g., a doctor or a Primary Care Provider (PCP), should be able to search over data of many patients. Also, the existing searchable encryption schemes do not allow the user to customize his search scope to a subset of documents related to his focus based on some search conditions. So, the user receives unrelated documents to his search focus and spends too much computation overhead to decrypt them which makes the search process inefficient. In this paper, we propose a customized searchable encryption scheme with efficient key management over medical cloud data. Each data owner, e.g, a patient, sends to the cloud server an encrypted index with each document and it contains the keywords of this document. Each doctor sends to the cloud server an encrypted trapdoor contains the keyword of the search. In a nutshell, the cloud server can compute the similarity score of the encrypted indices and the encrypted trapdoor without learning the keywords of the document and the query, and then it returns to the doctors the documents of his interest. By using customized search, doctor can limit his search scope to a subset of documents related to his interest based on some search conditions. Our formal analysis of the privacy preservation and performance evaluation indicate that our scheme is secure, expressive, and efficient compared to the literature’s existing approaches.

Ride-sharing is a service that enables drivers to share trips with riders, which leads to several benefits such as sharing the travel cost and reducing traffic congestion. However, most of the existing ride-sharing systems rely on a central trusted unit to organize the service, which makes them subject to a single point of failure and attack, and lack of transparency. A few works have investigated decentralized ride-sharing systems, but they either do not consider privacy preservation or suffer from a tradeoff between privacy protection and accuracy due to using location cloaking technique. This paper proposes a Blockchain-based ride sharing organization system with accurate matching and privacy preservation. To achieve the accurate matching, instead of representing the ride-sharing area by a single grid, it is represented by several overlapping grids so that only near drivers/riders share rides. To preserve privacy, drivers/riders encrypt their offers/requests using a lightweight cryptosystem, and the Blockchain matches the encrypted offers and requests without being able to decrypt them. Our security and privacy analysis demonstrate that our system can organize the ride-sharing service in a secure and transparent way, and also preserve the privacy of drivers and riders. To evaluate the performance of our system, we have implemented it, and our measurements indicate that our system requires low communication and computation overheads.

There are different metering systems adopted in the advanced metering infrastructure (AMI) of the smart grid. Among these systems, the net-metering is a promising system that motivates customers to install renewable resources at their premises to generate electricity and sell it to the utility. In this system, the customer’s home is equipped with one smart meter to report the net readings representing the difference between the power consumed from the power grid and the power injected into the grid. However, malicious customers may compromise their meters to report false readings to the utility to illegally achieve financial gains. This not only causes huge losses to the utility, but also deteriorates the grid performance. To the best of our knowledge, this problem has not been investigated. Therefore, in this paper, we investigate the detection of false-reading attacks in the net-metering system for the first time. Specifically, we propose four sophisticated attacks customized for the net-metering system and use them to create a dataset containing both benign and malicious samples. We have analyzed the dataset and detected time correlations between the readings within the benign samples. Based on the data analysis, we propose a general deep-learning-based detector with hybrid architecture involving convolutional neural network (CNN) and gated recurrent unit neural network (GRU). We have evaluated our detector, and the results demonstrate that the detector can detect the false-reading attacks with high precision and recall, and low false alarm.

There are two power consumption readings collection approaches adopted in the advanced metering infrastructure (AMI) of the smart grid; periodic transmission (PT) and change and transmit (CAT) AMI systems. Among these approaches, CAT is a promising approach that collects these readings efficiently by sending the readings only when there is enough change in consumption to reduce the number of transmitted readings. However, CAT AMI system suffers from electricity theft cyber-attacks that can be launched by malicious customers who may compromise their meters and manipulate their power consumption readings to illegally reduce their bills. These attacks do not only cause hefty financial losses but may also degrade the grid performance because the readings are used for grid management. Therefore, this paper is the first work that investigates this problem for CAT AMI system, in which the power consumption readings are not sent periodically to the system operator. We first prepare a benign dataset for the CAT AMI by processing a real power consumption readings dataset. Next, we propose a new set of attacks tailored for the CAT AMI to create a malicious dataset. Then, we propose a general and hybrid deep-learning electricity theft detector to identify malicious customers. The proposed detector is trained on both benign and malicious data from all customers using the reported CAT readings. Extensive test studies are carried out to investigate the detector’s performance using publicly available real data of power consumption from 114 customers. Simulation results demonstrate our models can detect malicious customers with high detection rate and low false alarm.

Reporting fine-grained power consumption readings periodically in advanced metering infrastructure (AMI) results in transmitting a massive amount of data by each smart meter (SM). To collect these readings efficiently, change and transmit (CAT) approach can be used. In CAT, the SM sends a consumption reading only when there is enough change in the consumption, which reduces the number of transmitted readings. However, using the CAT approach may trigger attackers to launch a presence-privacy attack (PPA) to infer sensitive information such as the absence of the house occupants by analyzing their SM’s transmission pattern. Therefore, in this paper, we propose a scheme, called “STID”, for collecting the power consumption readings efficiently in AMI networks while preserving the consumers’ privacy by transmitting spoofing transmissions based on an interactive deep-learning defense model. First, we create a dataset that contains the CAT transmission patterns using real power consumption readings and a clustering technique. Next, we train a deep-learning-based attacker model to launch PPA, and the results indicate that the success rate of the attacker is about 90%. Finally, to mitigate the PPA, we train a defense model using deep-learning to transmit spoofing transmissions. The evaluations of our envisioned STID scheme demonstrate a significant reduction in the attacker’s success rate while achieving high efficiency in terms of the number of readings that should be transmitted. Our measurements indicate that our proposed STID can reduce the attacker’s success rate to 6.12% and increase efficiency by about 38% compared to transmitting readings periodically.

Spatial-temporal charging coordination mechanisms are developed to avoid electrical overload at the charging stations and extravagant waiting time for electric vehicle drivers. Though, attackers could attack these mechanisms by launching distributed attacks against charging stations to prevent legitimate drivers from charging their vehicles. To attack a charging station, an attacker can compromise a set of vehicles, e.g., by disseminating a malware, and instruct them to send fake charging requests simultaneously to reserve the available energy capacity that is provided to a charging station without having the intention for charging, and thus benign vehicles do not find charging slots. This paper introduces an anomaly-based detection technique to identify the charging stations under this denial of charge (DoC) attacks using convolutional neural networks. The main idea is that each charging station has a normal energy demand pattern and launching DoC attacks changes this pattern. To capture such anomalous pattern, we use convolutional neural model to capture the temporal features within the demand of the charging station. To train our anomaly detector, we first create a benign dataset that could be utilized in other research areas such as load forecast and energy management. Then, we introduce a group of attacks that are used to create the malicious dataset. Finally, we used the benign and malicious datasets to train and test the deep neural model to detect DoC attacks. Our experiments show that our detector has high detection and low false alarm rates.

In advanced metering infrastructure, smart meters (SMs) send fine-grained power consumption readings periodically to the utility for load monitoring and energy management. Change and transmit (CAT) is an efficient approach to collect these readings, where the readings are not transmitted when there is no enough change in consumption. However, this approach causes a privacy problem, that is, by analyzing the transmission pattern of an SM, sensitive information on the house dwellers can be inferred. For instance, since the transmission pattern is distinguishable when dwellers are on travel, attackers may analyze the pattern to launch a presence-privacy attack (PPA) to infer whether the dwellers are absent from home. In this article, we propose a scheme, called “STDL,” for efficient collection of power consumption readings in advanced metering infrastructure (AMI) networks while preserving the consumers’ privacy by sending spoofing transmissions using a deep-learning approach. We first use a clustering technique and real power consumption readings to create a data set for transmission patterns using the CAT approach. Then, we train a deep-learning-based attacker model, and our evaluations indicate that the attacker’s success rate is about 91%. Finally, we train a deep-learning-based defense model to send spoofing transmissions efficiently to thwart the PPA. Extensive evaluations are conducted, and the results indicate that our scheme can reduce the attacker’s success rate to 3.15%, while still achieving high efficiency in terms of the number of readings that should be transmitted. Our measurements indicate that the proposed scheme can increase efficiency by about 41% compared to continuously transmitting readings.

In advanced metering infrastructure (AMI), smart meters (SMs) are installed at the consumer side to send fine-grained power consumption readings periodically to the system operator (SO) for load monitoring, energy management, and billing. However, fraudulent consumers launch electricity theft cyber attacks by reporting false readings to reduce their bills illegally. These attacks do not only cause financial losses but may also degrade the grid performance because the readings are used for grid management. To identify these attackers, the existing schemes employ machine-learning models using the consumers' fine-grained readings, which violates the consumers' privacy by revealing their lifestyle. In this article, we propose an efficient scheme that enables the SO to detect electricity theft, compute bills, and monitor load while preserving the consumers' privacy. The idea is that SMs encrypt their readings using functional encryption (FE), and the SO uses the ciphertexts to: 1) compute the bills following the dynamic pricing approach; 2) monitor the grid load; and 3) evaluate a machine-learning model to detect fraudulent consumers, without being able to learn the individual readings to preserve consumers' privacy. We adapted an FE scheme so that the encrypted readings are aggregated for billing and load monitoring and only the aggregated value is revealed to the SO. Also, we exploited the inner-product operations on encrypted readings to evaluate a machine-learning model to detect fraudulent consumers. The real data set is used to evaluate our scheme, and our evaluations indicate that our scheme is secure and can detect fraudulent consumers accurately with low communication and computation overhead.

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.

Over the last few years, finding vacant parking spaces has become a hassle for drivers especially in crowded cities. This problem leads to wasting drivers' time, traffic congestion, and air pollution. Recently, smart parking systems aim to address this problem by enabling drivers to have real-time parking information about vacant parking spaces. However, the existing parking systems rely on a central third party to organize the service, which makes them subject to a single point of failure and privacy breach concerns by both internal and external attackers. In this paper, we propose a secure smart parking system using blockchain technology. Specifically, a consortium blockchain is made of parking lots to ensure security, transparency, and availability of the parking system. Then, to protect the drivers' location privacy, we use cloaking technique to hide the drivers' locations. The blockchain validators return available parking offers with in the cloaked area. Finally, the driver selects the best offer and makes reservation directly with the parking lot. Evaluations are conducted to evaluate the proposed scheme, and results indicate practicality of our scheme.

Purveyors of malicious network attacks continue to increase the complexity and the sophistication of their techniques, and their ability to evade detection continues to improve as well. Hence, intrusion detection systems must also evolve to meet these increasingly challenging threats. Machine learning is often used to support this needed improvement. However, training a good prediction model can require a large set of labeled training data. Such datasets are difficult to obtain because privacy concerns prevent the majority of intrusion detection agencies from sharing their sensitive data. In this paper, we propose the use of mimic learning to enable the transfer of intrusion detection knowledge through a teacher model trained on private data to a student model. This student model provides a mean of publicly sharing knowledge extracted from private data without sharing the data itself. Our results confirm that the proposed scheme can produce a student intrusion detection model that mimics the teacher model without requiring access to the original dataset.

Preserving the customers' privacy, while collecting their power consumption for monitoring and billing, is a prime concern in an Advanced Metering Infrastructure (AMI) network of the Smart Grid (SG). In this paper, we address this concern by formally formulating the data aggregation privacy problem, and propose a uniquely crafted Privacy-Preserving Monitoring and Billing scheme using Functional Encryption, referred to as PMBFE. Our proposed PMBFE fulfills four key objectives: (i) data aggregation for billing, (ii) dynamic pricing flexibility, (iii) load monitoring with customers' privacy preservation; and (iv) analysis on how the adopted functional encryption is able to jointly perform data aggregation efficiently and guarantee privacy-preservation. Our envisioned PMBFE approach is evaluated with extensive computer-based simulations. In contrast with the widely employed homomorphic-based encryption in AMI networks, our proposed PMBFE demonstrates significant performance improvement in terms of both communication and computation overheads while guaranteeing user-data privacy. Furthermore, the conducted security analysis exhibits the robustness of our proposal against collusion and eavesdropping attacks.

Ride-sharing is a service that enables drivers to share their trips with other riders, contributing to improving traffic congestion as well as assist in reducing Carbon Dioxide (CO2) emission and fuel consumption. It has come to the forefront in recent years as a Green service in large cities. However, the majority of existing ride-sharing services rely on a central third party, which makes them subject to a single point of failure and privacy disclosure concerns by both internal and external attackers. Moreover, they are vulnerable to distributed denial of service (DDoS) and Sybil attacks due to malicious users. There is also high service fees paid to the ride-sharing service provider. In this paper, we propose to decentralize ride-sharing services based on a public Blockchain. Our scheme enables drivers to propose ride-sharing services without relying on a trusted third party. To preserve location privacy, riders send cloaked ride requests to hide their exact pick-up/drop-off locations, and departure/arrival dates. Then, by using an off-line matching technique, drivers sends their offers encrypted to ensure data confidentiality. Upon receiving the ride-offers, the rider can find a ride match using some heuristics as well as the bid price included in the offer. To preserve anonymity, riders/drivers use pseudonyms that change per trip to ensure unlinkabilty. We envision the application of this technology in Green Internet of Things connected smart cities, where ride sharing services are common. Finally, we implement our scheme and deploy it in a test net of Ethereum. The experimental results show the applicability of our protocol.

The proliferation of renewable energy systems and high-capacity batteries has enabled customers to trade their excess energy on the market in a peer-to-peer manner through the smart grid. At the same time, electric vehicles (EVs) are enjoying widespread acceptance, leading to a higher demand for charging stations. In this paper, we propose a system where energy traders and EV owners collectively work to satisfy the energy demands of EVs. Specifically, energy traders make bids to EV owners who, in turn, reserve their preferred charging station for a specific period of time. To protect the privacy of EV owners, we also introduce an anonymous payment system that cannot link individual owners to specific charging locations. Finally, to guarantee the security and transparency of the entire system, we store all transactions on a consortium blockchain that is managed by the energy traders and the financial institutions that support the anonymous payment system. Our experimental results indicate that the overhead of the cryptographic operations involved in the major transactions is low, in terms of both computational and communication cost.

Recently, Autonomous Vehicles (AVs) have gained extensive attention from both academia and industry. AVs are a complex system composed of many subsystems, making them a typical target for attackers. Therefore, the firmware of the different subsystems needs to be updated to the latest version by the manufacturer to fix bugs and introduce new features, e.g., using security patches. In this paper, we propose a distributed firmware update scheme for the AVs' subsystems, leveraging blockchain and smart contract technology. A consortium blockchain made of different AVs manufacturers is used to ensure the authenticity and integrity of firmware updates. Instead of depending on centralized third parties to distribute the new updates, we enable AVs, namely distributors, to participate in the distribution process and we take advantage of their mobility to guarantee high availability and fast delivery of the updates. To incentivize AVs to distribute the updates, a reward system is established that maintains a credit reputation for each distributor account in the blockchain. A zero-knowledge proof protocol is used to exchange the update in return for a proof of distribution in a trustless environment. Moreover, we use attribute-based encryption (ABE) scheme to ensure that only authorized AVs will be able to download and use a new update. Our analysis indicates that the additional cryptography primitives and exchanged transactions do not affect the operation of the AVs network. Also, our security analysis demonstrates that our scheme is efficient and secure against different attacks.

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 EPIC, an efficient and privacy-preserving data collection scheme with EtoE data integrity verification for advanced metering infrastructure networks. Using efficient cryptographic operations, each meter should send a masked reading to the utility such that all the masks are canceled after aggregating all meters' masked readings, and thus the utility can only obtain an aggregated reading to preserve consumers' privacy. The utility can verify the aggregated reading integrity without accessing the individual readings to preserve privacy. It can also identify the attackers and compute electricity bills efficiently by using the fine-grained readings without violating privacy. Furthermore, EPIC can resist collusion attacks in which the utility colludes with a relay node to extract the meters' readings. A formal proof and probabilistic analysis are used to evaluate the security of EPIC, and ns-3 is used to implement EPIC and evaluate the network performance. In addition, we compare EPIC to existing data collection schemes in terms of overhead and security/privacy features.

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.

Modern smart grids rely on advanced metering infrastructure (AMI) networks for monitoring and billing purposes. However, such an approach suffers from electricity theft cyberattacks. Different from the existing research that utilizes shallow, static, and customer-specific-based electricity theft detectors, this paper proposes a generalized deep recurrent neural network (RNN)-based electricity theft detector that can effectively thwart these cyberattacks. The proposed model exploits the time series nature of the customers' electricity consumption to implement a gated recurrent unit (GRU)-RNN, hence, improving the detection performance. In addition, the proposed RNN-based detector adopts a random search analysis in its learning stage to appropriately fine-tune its hyper-parameters. Extensive test studies are carried out to investigate the detector's performance using publicly available real data of 107,200 energy consumption days from 200 customers. Simulation results demonstrate the superior performance of the proposed detector compared with state-of-the-art electricity theft detectors.

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.

The future smart grid will enable homes to have energy storage units that can store the excess power generated from renewable energy sources and sell it to the grid during the peak hours. Realization of this process, however, requires the utility company to be able to communicate with the storage units whenever needed. Nonetheless, the security and the privacy of this communication is essential to not only ensure a fair energy selling market but also eliminate any privacy concerns of the users due to potential exposure of their energy levels. In this paper, we propose a secure and privacy-preserving power injection querying scheme by exploiting the already available advanced metering infrastructure (AMI) and long-term evolution (LTE) cellular networks. The idea is based on collecting power injection bids from storage units and sending their aggregated value to the utility rather than the individual bids in order to preserve user privacy. We also develop a bilinear pairing-based technique to enable the utility company to ensure the integrity and authenticity of the aggregated bid without accessing the individual bids. In this way, no party will have access to the storage units' individual bids and use them to achieve unfair financial gains. We implemented the proposed scheme in an integrated AMI/LTE network using the ns-3 network simulator. Our evaluations have demonstrated that the proposed scheme is secure and can protect user privacy with acceptable communication and computation overhead.

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.

Given the scalability of the advanced metering infrastructure (AMI) networks, maintenance and access of certificate revocation lists (CRLs) pose new challenges. It is inefficient to create one large CRL for all the smart meters (SMs) or create a customized CRL for each SM since too many CRLs will be required. In order to tackle the scalability of the AMI network, we divide the network into clusters of SMs, but there is a tradeoff between the overhead at the certificate authority (CA) and the overhead at the clusters. We use Bloom filters to reduce the size of the CRLs in order to alleviate this tradeoff by increasing the clusters' size with acceptable overhead. However, since Bloom filters suffer from false positives, there is a need to handle this problem so that SMs will not discard important messages due to falsely identifying the certificate of a sender as invalid. To this end, we propose two certificate revocation schemes that can identify and nullify the false positives. While the first scheme requires contacting the gateway to resolve them, the second scheme requires the CA additionally distribute the list of certificates that trigger false positives. Using mathematical models, we have demonstrated that the probability of contacting the gateway in the first scheme and the overhead of the second scheme can be very low by properly designing the Bloom filters. In order to assess the scalability and validate the mathematical formulas, we have implemented the proposed schemes using Visual C. The results indicate that our schemes are much more scalable than the conventional CRL and the mathematical and simulation results are almost identical. Moreover, we simulated the distribution of the CRLs in a wireless mesh-based AMI network using ns-3 network simulator and assessed its distribution overhead.

Mobile healthcare system integrating wearable sensing and wireless communication technologies continuously monitors the users' health status. However, the mHealth system raises a severe privacy concern as the data it collects are private information, such as heart rate and blood pressure. In this paper, we propose an efficient and privacy-preserving mHealth data release approach for the statistic data with the objectives to preserve the unique patterns in the original data bins. The proposed approach adopts the bucket partition algorithm and the differential privacy algorithm for privacy preservation. A customized bucket partition algorithm is proposed to combine the database value bins into buckets according to certain conditions and parameters such that the patterns are preserved. The differential privacy algorithm is then applied to the buckets to prevent an attacker from being able to identify the small changes at the original data. We prove that the proposed approach achieves differential privacy. We also show the accuracy of the proposed approach through extensive simulations on real data. Real experiments show that our partitioning algorithm outperforms the state-of-the-art in preserving the patterns of the original data by a factor of 1.75.

In this paper, we propose an efficient scheme that utilizes symmetric-key-cryptography and hashing operations to collect consumption data. The idea is based on sending masked power consumption readings from the meters and removing these masks by adding all the meters' messages, so that the utility can learn the aggregated reading but cannot learn the individual readings. We also introduce a key management procedure that uses asymmetric key operations, but unlike the power consumption collection that is done very frequently, the key management procedure is run every long time for key renewals. Our evaluations indicate that the cryptographic operations needed in our scheme are much more efficient than the operations needed in the existing schemes. In addition, we have shown that the proposed scheme can preserve the consumers' privacy and provide high protection level against collusion attacks. Finally, ns-3 simulation results demonstrate that the network performance of the proposed scheme outperforms the performance of the existing schemes due to reducing the packet size and computational overhead.

In this paper, we propose a secure and efficient handover scheme for the Long Term Evolution-Advanced (LTE-A) networks. The proposed scheme does not trust the basestations because they may be accessible to attackers and operated by subscribers, rather than service providers. First, we propose a registration procedure to enable the base-stations to authenticate and register with the Home Subscriber Server (HSS). Then, we propose a procedure to enable the user equipment (UEs) to authenticate and exchange keys with the Mobility Management Entity (MME) and base-stations. Finally, we propose a secure and fast handover procedure. To reduce the handover latency, the HSS is not involved and the computation overhead on the UEs is very low. The proposed scheme is uniform in the sense that one procedure can be used for all handover scenarios. Our security analysis demonstrates that the proposed scheme can thwart well-known attacks such as impersonation, man in the middle, packet replay, etc. The proposed key agreement procedures can achieve backward/forward secrecy, where attackers cannot derive the past or future session keys. Our performance evaluation results demonstrate that the proposed handover scheme is fast because it needs few computations and exchanges few number of packets. This is important to improve the quality of service, avoid call termination, and service disruption. Moreover, the proposed scheme imposes minimal overhead on the mobile nodes, which is very desirable because these nodes usually have low computational power and energy.

In this paper, we propose a privacy-preserving power charging coordination scheme. Each energy storage unit (ESU) should send a charging request to an aggregator. The request does not reveal any private information to the aggregator. The aggregator forwards the requests to a charging controller that can know enough data to run a charging coordination scheme, but it cannot link the data to particular ESUs. Temporal charging coordination scheme is then proposed based on a modified knapsack problem formulation. The goal is to maximize the amount of power delivered to the ESUs before the charging requests expire without exceeding the available maximum charging capacity. Our simulation results demonstrate that both the optimal charging coordination and the privacy-aware charging coordination exhibit an improved performance compared with a first-come-first-serve charging coordination. More importantly, the privacy-aware scheme offers an attractive trade-off between the charging coordination performance and privacy preservation.

In this paper, we propose a trust-based and privacy-preserving fine-grained data retrieval scheme for mobile social networks (MSNs). The scheme enables users to create a log of trusted users who store (or are interested in) some topics related to a subject of interest. A subject is a broad term that can cover many fine-grained topics. In creating logs, we leverage friends-of-friends relationships and transferrable trust concept. Each user trusts its friends and the friends of friends. If a friend is not interested in a subject, he can help his friend in creating the log by linking the friend to his friends without knowing the subject to preserve privacy. In order to reduce the storage and computation overhead, we use Bloom filters to store the topics. A distinctive feature in our scheme is that it can query users who possess a fine-grained topic, rather than querying users who are interested in the broad subject but they may not have the specific topic of interest. We analyze the security and privacy of our scheme and evaluate the communication and computation overhead.

While the newly envisioned smart(er) grid (SG) will result in a more efficient and reliable power grid, its collection and use of fine-grained meter data has widely raised concerns on consumer privacy. While a number of approaches are available for preserving consumer privacy, these approaches are mostly not very practical to be used due to two reasons. 1) Since the data is hidden, this reduces the ability of the utility company to use the data for distribution state estimation. 2) The approaches were not tested under realistic wireless infrastructures that are currently in use. In this paper, we propose to implement a meter data obfuscation approach to preserve consumer privacy that has the ability to perform distribution state estimation. We then assess its performance on a large-scale advanced metering infrastructure (AMI) network built upon the new IEEE 802.11s wireless mesh standard. For the data obfuscation approach, we propose two secure obfuscation value distribution mechanisms on this 802.11s-based wireless mesh network (WMN). Using obfuscation values provided via this approach, the meter readings are obfuscated to protect consumer privacy from eavesdroppers and the utility companies while preserving the utility companies' ability to use the data for state estimation. We assessed the impact of this approach on data goodput, delay, and packet delivery ratio (PDR) under a variety of conditions. Simulation results have shown that the proposed approach can provide very similar performance to that of nonprivacy approach with negligible overheads on the meters and network.

Wireless communication capabilities of Vehicular Ad Hoc Networks (VANETs) have been utilized in various cutting-edge applications such as Vehicular Social Networks (VSNs). One of the benefits of VSNs is sharing of common-interest information among vehicle drivers. Drivers may benefit from identifying neighbors that have interest common with them along with the extent of their interest. However, there are some privacy issues that should be addressed. Revealing the nature and degree of interests (DOI) of drivers can be in violation of their privacy. In this paper, we propose an efficient chatting scheme among drivers that preserves such privacy. We use attribute based encryption (ABE) technique for anonymous common interest verification and homomorphic encryption technique for anonymous DOI verification. Moreover, we propose an efficient search mechanism to enable vehicles to check if they have common interests with low computation and communication overhead. To secure conversation, a key agreement protocol is used to enable the drivers that have the same interest and the desired DOI to establish a shared secret key. Our extensive evaluations demonstrate that our scheme can successfully preserve drivers' privacy with low communication and computation overhead.

In Vehicular Ad Hoc Networks (VANETs), the roadside units (RSUs) need to know the number of vehicles in their vicinity to be used in traffic management. However, an attacker may launch a Sybil attack by pretending to be multiple simultaneous vehicles. This attack is severe when a vehicle colludes with others to use valid credentials to authenticate the Sybil vehicles. If RSUs are unable to identify such an attack, they will report wrong number of vehicles to the traffic management center, which may result in disseminating wrong traffic instructions to vehicles. In this paper, we propose a cross-layer scheme to enable the RSUs to identify such Sybil vehicles. Since Sybil vehicles do not exist in their claimed locations, our scheme is based on verifying the vehicles' locations. A challenge packet is sent the vehicle's claimed location using directional antenna to detect the presence of a vehicle. If the vehicle is at the expected location, it should be able to receive the challenge and send back a valid response packet. In order to reduce the overhead and instead of sending challenge packets to all the vehicles all the time, packets are sent only when there is a suspicion of Sybil attack. We also discuss several Sybil attack alarming techniques. The evaluation results demonstrate that our scheme can achieve high detection rate with low probability of false alarm. Additionally, the scheme requires acceptable communication and computation overhead.

With the large increase in the number of registered vehicles, the congestion and slow traffic problems are expected to worsen. Vehicular Ad Hoc Networks (VANETs) can play a great role in avoiding these problems by sending guidance to vehicles to pursue alternative routes. However, the published schemes require vehicles to report their future routes which can seriously violate privacy. In this paper, we present privacy-preserving route reporting scheme that suits VANET-enabled traffic management rather than warning vehicles after congestion happens. Vehicles provide encrypted segment-based route information to road side units (RSUs). Instead of sending one message for each route segment, all the segments' data can be collected by one message using homomorphic encryption. RSUs compute the encryption of the expected number of vehicles in each segment of the road without knowing the actual routes of vehicles. Each RSU shares the vehicles' routes information with a traffic management center (TMC), which decrypts the expected total number of vehicles at different segments of the road without knowing the individual vehicles' routes. Then, it conducts analysis and sends predictions and recommendations back to the RSUs. Passing vehicles solicit hints from RSUs on the expected traffic condition in order to decide to take an alternating route if there is a potential of congestion or slow traffic in its main route. Our analysis and evaluation results demonstrate that our scheme can preserve the privacy of the drivers' future routes in an efficient and secure way.

In smart grid Automatic Metering Infrastructure (AMI) networks, smart meters should send consumption data to the utility company (UC) for grid state estimation. Creating a new infrastructure to support this communication is costly and may take long time which may delay the deployment of the AMI networks. The Long Term Evolution-Advanced (LTE-A) networks can be used to support the communications between the AMI networks and the UC. However, since these networks are owned and operated by private companies, the UC cannot ensure the security and privacy of the communications. Moreover, the data sent by the AMI networks have different characteristics and requirements than most of the existing applications in LTE-A networks. For example, there is a strict data delay requirement, data is short and transmitted every short time, data is sent at known/predefined time slots, and there is no handover. In this paper, we study enabling secure and privacy preserving AMI-UC communications via LTE-A networks. The proposed scheme aims to achieve essential security requirements such as authentication, confidentiality, key agreement and data integrity without trusting the LTE-A networks. Furthermore, an aggregation scheme is used to protect the privacy of the electricity consumers. It can also reduce the amount of required bandwidth which can reduce the communication cost. Our evaluations have demonstrated that our proposals are secure and require low communication/computational overhead.

The public key cryptography (PKC) is essential for securing many applications in smart grid. For the secure use of the PKC, certificate revocation schemes tailored to smart grid applications should be adopted. However, little work has been done to study certificate revocation in smart grid. In this paper, we first explain different motivations that necessitate revoking certificates in smart grid. We also identify the applications that can be secured by PKC and thus need certificate revocation. Then, we explain existing certificate revocation schemes and define several metrics to assess them. Based on this assessment, we identify the applications that are proper for each scheme and discuss how the schemes can be modified to fully satisfy the requirements of its potential applications. Finally, we study certificate revocation in pseudonymous public key infrastructure (PPKI), where a large number of certified public/private keys are assigned for each node to preserve privacy. We target vehicles-to-grid communications as a potential application. Certificate revocation in this application is a challenge because of the large number of certificates. We discuss an efficient certificate revocation scheme for PPKI, named compressed certificate revocation lists (CRLs). Our analytical results demonstrate that one revocation scheme cannot satisfy the overhead/security requirements of all smart grid applications. Rather, different schemes should be employed for different applications. Moreover, we used simulations to measure the overhead of the schemes.

In vehicular ad hoc networks, vehicles should report events to warn the drivers of unexpected hazards on the roads. While these reports can contribute to safer driving, vehicular ad hoc networks suffer from various security threats; a major one is Sybil attacks. In these attacks, an individual attacker can pretend as several vehicles that report a false event. In this paper, we propose a secure event-reporting scheme that is resilient to Sybil attacks and preserves the privacy of drivers. Instead of using asymmetric key cryptography, we use symmetric key cryptography to decrease the computation overhead. We propose an efficient pseudonym generation technique. The vehicles receive a small number of long-term secrets to compute pseudonyms/keys to be used in reporting the events without leaking private information about the drivers. In addition, we propose a scheme to identify the vehicles that use their pool of pseudonyms to launch Sybil attacks without leaking private information to road side units. We also study a strong adversary model assuming that attackers can share their pool of pseudonyms to launch colluding Sybil attacks. Our security analysis and simulation results demonstrate that our scheme can detect Sybil attackers effectively with low communication and computation overhead. Copyright © 2015John Wiley & Sons, Ltd.

We propose E-STAR for establishing stable and reliable routes in heterogeneous multihop wireless networks. E-STAR combines payment and trust systems with a trust-based and energy-aware routing protocol. The payment system rewards the nodes that relay others’ packets and charges those that send packets. The trust system evaluates the nodes’ competence and reliability in relaying packets in terms of multi-dimensional trust values. The trust values are attached to the nodes’ public-key certificates to be used in making routing decisions. We develop two routing protocols to direct traffic through those highly-trusted nodes having sufficient energy to minimize the probability of breaking the route. By this way, E-STAR can stimulate the nodes not only to relay packets, but also to maintain route stability and report correct battery energy capability. This is because any loss of trust will result in loss of future earnings. Moreover, for the efficient implementation of the trust system, the trust values are computed by processing the payment receipts. Analytical results demonstrate that E-STAR can secure the payment and trust calculation without false accusations. Simulation results demonstrate that our routing protocols can improve the packet delivery ratio and route stability.

Given the large geographic deployment and scalability of the Advanced Metering Infrastructure (AMI) networks, it is inefficient to create one large certificate revocation list (CRL) for all the networks. It is also inefficient to create a CRL for each meter having the certificates it needs because too many CRLs will be required. It is beneficial to balance the size of the CRLs and the overhead of forming and distributing them. In this paper, the certificate authority (CA) groups the AMI networks and composes one CRL for each group. We use Bloom filter to reduce the number of CRLs by increasing the groups size with acceptable overhead on the meters. However, Bloom filters suffer from false positives which is not acceptable in AMI networks because meters may miss important messages. We propose a novel scheme to identify and mitigate the false positives by making use of the fact that Bloom filters are free of false negatives. The meters should contact the gateway to resolve the false positives. We use Merkle tree to enable the gateway to provide efficient proof for certificate revocation without contacting the CA. We derive a mathematical formula to the probability of contacting the gateway as a function of the filter's parameters. We will show that this probability can be low by properly designing the Bloom filter. In order to assess the performance and the applicability of the proposed scheme, we use ns-3 network simulator to implement the scheme in a IEEE 802.11s-based mesh AMI networks. The results demonstrate that our scheme can be used efficiently for AMI networks.

We propose lightweight protocol for securing communication and preserving users' anonymity and location privacy in hybrid ad hoc networks. Symmetric-key-cryptography operations and payment system are used to secure route discovery and data transmission. To reduce the overhead, the payment can be secured without submitting or processing payment proofs (receipts). To preserve users' anonymity with low overhead, we develop efficient pseudonym generation and trapdoor techniques that do not use the resource-consuming asymmetric-key cryptography. Pseudonyms do not require large storage area or frequently contacting a central unit for refilling. Our trapdoor technique uses only lightweight hashing operations. This is important because trapdoors may be processed by a large number of nodes. Developing low-overhead secure and privacy-preserving protocol is a real challenge due to the inherent contradictions: 1) securing the protocol requires each node to use one authenticated identity, but a permanent identity should not be used for privacy preservation; and 2) the low overhead requirement contradicts with the large overhead usually needed for preserving privacy and securing the communication. Our analysis and simulation results demonstrate that our protocol can preserve privacy and secure the communication with low overhead.

The public key cryptography will play an essential role in securing the smart grid communications. For the secure use of the public key cryptography, an efficient and secure certificate revocation scheme specially tailored to smart grid architecture should be adopted. In this paper, we study certificate revocation in smart grid and design efficient and scalable certificate revocation schemes. The schemes have different security strengths and require different overhead levels. We also propose an efficient certificate revocation scheme for pseudonymous public key infrastructure using compressed certificate revocation lists. Analytical results demonstrate that using revocation schemes is essential for securing smart grid, and the proposed schemes are secure. Moreover, simulation results demonstrate that the proposed schemes require low overhead.

The public-key cryptography is indispensable for securing the smart grid communications. In this paper, we propose a hierarchical and fully-connected public key infrastructure that considers the smart grid characteristics. In the proposed public key infrastructure, each certificate authority is responsible for managing the public-key certificates for a geo-bounded small area. We also propose a novel format for the certificates that does not only bind a node's identity to its public key but also to its privileges and permissions. Finally we propose efficient and scalable certificate- renewing scheme that can much reduce the overhead of renewing certificates. Our verifications and evaluations demonstrate that using public key cryptography is essential for securing the smart grid and our proposals are scalable. Moreover, the simulation results demonstrate that the certificate-renewing scheme can significantly reduce the overhead of certificate renewals.

We propose RACE, a report-based payment scheme for multihop wireless networks to stimulate node cooperation, regulate packet transmission, and enforce fairness. The nodes submit lightweight payment reports (instead of receipts) to the accounting center (AC) and temporarily store undeniable security tokens called Evidences. The reports contain the alleged charges and rewards without security proofs, e.g., signatures. The AC can verify the payment by investigating the consistency of the reports, and clear the payment of the fair reports with almost no processing overhead or cryptographic operations. For cheating reports, the Evidences are requested to identify and evict the cheating nodes that submit incorrect reports. Instead of requesting the Evidences from all the nodes participating in the cheating reports, RACE can identify the cheating nodes with requesting few Evidences. Moreover, Evidence aggregation technique is used to reduce the Evidences' storage area. Our analytical and simulation results demonstrate that RACE requires much less communication and processing overhead than the existing receipt-based schemes with acceptable payment clearance delay and storage area. This is essential for the effective implementation of a payment scheme because it uses micropayment and the overhead cost should be much less than the payment value. Moreover, RACE can secure the payment and precisely identify the cheating nodes without false accusations.

In habitat monitoring applications, when a sensor node detects an endangered animal, e.g., a panda, it reports the animal's presence and activities to the sink. However, the adversaries can eavesdrop on the network transmissions and make use of the traffic information to locate pandas to hunt them. In this paper, we first define hotspot phenomenon that causes an obvious inconsistency in the network traffic pattern due to the large volume of packets originated from a small spot. Second, we develop a realistic adversary model assuming that the adversary can monitor the network traffic in multiple areas rather than the entire network or only one area. We then introduce a novel attack called Hotspot-Locating where the adversary uses traffic analysis techniques to locate hotspots. Simulation and analytical results demonstrate that Hotspot-Locating attack is a severe threat to the source nodes' location privacy and the existing routing-based privacy preserving schemes are vulnerable to this attack because they leak traffic analysis information that can be used to locate the source nodes. For stronger privacy preservation, the traffic analysis information such as packet correlation and the nodes' packet sending rates should be concealed.

In this paper, we propose a novel scheme for efficiently and securely preserving source nodes' location privacy. Our scheme uses efficient cryptographic operations to change the packets' appearance at each hop to prevent packet correlation. It also creates a cloud with irregular shape of fake traffic to enable the real source node to send its data anonymously to a fake source node to send to the sink and to camouflage the real source node in the nodes creating the cloud. To reduce the energy cost, clouds are active only during data transmission and the intersection of clouds creates a larger merged cloud to reduce the number of fake packets and boost privacy preservation. Simulation and analytical results demonstrate that our scheme can provide stronger privacy preservation than routing-based schemes and requires much less energy cost than global-adversary-based schemes.

In multihop cellular networks, the mobile nodes usually relay others' packets for enhancing the network performance and deployment. However, selfish nodes usually do not cooperate but make use of the cooperative nodes to relay their packets, which has a negative effect on the network fairness and performance. In this paper, we propose a fair and efficient incentive mechanism to stimulate the node cooperation. Our mechanism applies a fair charging policy by charging the source and destination nodes when both of them benefit from the communication. To implement this charging policy efficiently, hashing operations are used in the ACK packets to reduce the number of public-key-cryptography operations. Moreover, reducing the overhead of the payment checks is essential for the efficient implementation of the incentive mechanism due to the large number of payment transactions. Instead of generating a check per message, a small-size check can be generated per route, and a check submission scheme is proposed to reduce the number of submitted checks and protect against collusion attacks. Extensive analysis and simulations demonstrate that our mechanism can secure the payment and significantly reduce the checks' overhead, and the fair charging policy can be implemented almost computationally free by using hashing operations.

In wireless sensor networks, adversaries can make use of the traffic information to locate the monitored objects, e.g., to hunt endangered animals or kill soldiers. In this paper, we first define a hotspot phenomenon that causes an obvious inconsistency in the network traffic pattern due to the large volume of packets originating from a small area. Second, we develop a realistic adversary model, assuming that the adversary can monitor the network traffic in multiple areas, rather than the entire network or only one area. Using this model, we introduce a novel attack called Hotspot-Locating where the adversary uses traffic analysis techniques to locate hotspots. Finally, we propose a cloud-based scheme for efficiently protecting source nodes' location privacy against Hotspot-Locating attack by creating a cloud with an irregular shape of fake traffic, to counteract the inconsistency in the traffic pattern and camouflage the source node in the nodes forming the cloud. To reduce the energy cost, clouds are active only during data transmission and the intersection of clouds creates a larger merged cloud, to reduce the number of fake packets and also boost privacy preservation. Simulation and analytical results demonstrate that our scheme can provide stronger privacy protection than routing-based schemes and requires much less energy than global-adversary-based schemes.

Security has been recognized as a key issue for the expansion of eHealth application, where highly sensitive patient's medical data are routed through a non-secure wireless network. In this paper, we look into the various security and privacy requirements for the eHealth application and propose an agent-based secure and trustworthy packet-forwarding Protocol (ASTP) considering the neighbor nodes previous and recent activities. ASTP incorporated with proper security tools that enhanced the overall performance of a cooperative multi-hop wireless network used for an eHealth application. The proposed protocol can successfully detects malicious nodes and the information is used and shared to the neighbors to avoid co-operating with them either for data forwarding, aggregation or any other cooperative function. Patient privacy is maintained by using an renewable pseudo-identity. Finally, security analysis and experimental results demonstrate that ASTP improves the average packet delivery ratio and maintains the require security and privacy at the cost of an acceptable communication delay.

In this paper, we propose a secure data-forwarding scheme, called SATS, for delay-tolerant wireless networks. SATS uses credits (or micropayment) to stimulate the nodes' cooperation in relaying other nodes' messages and to enforce fairness. SATS also makes use of a trust system to assign a trust value for each node. A node's trust value is high when the node actively forwards others' messages. The highly trusted nodes are preferable in data forwarding to avoid the Black-Hole attackers that drop messages intentionally to degrade the message delivery rate. In this way, SATS can stimulate the nodes' cooperation not only to earn credits but also to maintain high trust values to increase their chances to participate in future data forwarding. Our security evaluation demonstrates that SATS can secure the payment and trust calculation. The performance evaluation demonstrates that SATS can significantly improve the message delivery rate due to avoiding the Black-Hole attackers in message forwarding and stimulating the nodes' cooperation.

In this paper, we propose a receipt-free cooperation incentive scheme for multihop wireless networks. The nodes submit lightweight payment reports containing their alleged charges and rewards, and store undeniable security evidences. The fair reports can be cleared with almost no processing overhead. For the cheating reports, the evidences are requested to identify and evict the cheating nodes. Since cheating actions are exceptional, our scheme can significantly reduce the overhead of submitting and processing the payment data. Extensive analysis and simulations demonstrate that the proposed scheme can clear the payment with almost no processing overhead while achieving the same security strength as the receipt-based schemes.

Node cooperation in relaying others' packets and route stability are essential for high-performance multi-hop wireless networks and reliable data transmission. In this paper, we propose routing protocol called TETO for stimulating node cooperation and establishing stable routes. TETO uses credits (or micropayment) to stimulate the nodes' cooperation and processes the payment receipts to evaluate the nodes' quality of packet-relay in terms of trust values. Stable routes are established through the highly trusted nodes having sufficient residual energy. Extensive analysis and simulations demonstrate that TETO can secure the payment and trust calculation and significantly improve route stability and thus the packet delivery ratio.

In multihop wireless networks, selfish nodes do not relay other nodes' packets and make use of the cooperative nodes to relay their packets, which has negative impact on the network fairness and performance. Incentive protocols use credits to stimulate the selfish nodes' cooperation, but the existing protocols usually rely on the heavyweight public-key operations to secure the payment. In this paper, we propose secure cooperation incentive protocol that uses the public-key operations only for the first packet in a series and uses the lightweight hashing operations in the next packets, so that the overhead of the packet series converges to that of the hashing operations. Hash chains and keyed hash values are used to achieve payment nonrepudiation and thwart free riding attacks. Security analysis and performance evaluation demonstrate that the proposed protocol is secure and the overhead is incomparable to the public-key-based incentive protocols because the efficient hashing operations dominate the nodes' operations. Moreover, the average packet overhead is less than those of the public-key-based protocols with very high probability due to truncating the keyed hash values.

The existing credit-based mechanisms mainly focus on stimulating the rational packet droppers to relay other nodes' packets, but they cannot identify the irrational packet droppers such as compromised or broken nodes, which has negative impact on the network performance. In this paper, we propose a credit-based mechanism that uses credits to stimulate the rational packet droppers to cooperate, and uses reputation system to identify the irrational ones. Payment receipts are processed to reward the cooperative nodes, and to detect the broken links so that a reputation system can be built to identify the irrational packet droppers. Our evaluations demonstrate that our mechanism can secure the payment, and precisely identify the irrational packet droppers.

Secure cooperation incentive schemes usually use public key cryptography, which incur too heavy overhead to be used efficiently in limited-resource nodes. In this paper, we propose a novel incentive scheme that requires public-key operations only for the first packet in a series. The efficient hashing operations are used in the successive packets so that the overhead of the packet series converges to that of the hashing operations. Our evaluations demonstrate that the proposed scheme is secure and has much less overhead than the public key cryptography based incentive schemes.

In multi-hop wireless networks, the mobile nodes usually act as routers to relay packets generated from other nodes. However, selfish nodes do not cooperate but make use of the honest ones to relay their packets, which has negative effect on fairness, security, and performance of the network. In this paper, we propose a novel incentive mechanism to stimulate cooperation in multi-hop wireless networks. Fairness can be achieved by using credits to reward the cooperative nodes. The overhead can be significantly reduced by using a cheating detection system (CDS) to secure the payment. Extensive security analysis demonstrates that the CDS can identify the cheating nodes effectively under different cheating strategies. Simulation results show that the overhead of the proposed incentive mechanism is incomparable with the existing ones.

In this paper, we propose an incentive system to stimulate the nodes' cooperation in multi-hop wireless networks. Reducing the number and the size of the payment receipts is essential for practical implementation of an incentive system due to the high frequency of low-value transactions. First, the receipts' number is reduced by generating one fixed-size receipt per session instead of generating a receipt per packet or group of packets, and different receipts are aggregated to a reduced-size receipt. Second, reactive receipt submission mechanism is proposed to reduce the submitted receipts' number and protect against collusion attacks. Extensive analysis and simulations demonstrate that our incentive system can secure the payment, and reduce the overhead of storing, submitting, and processing the receipts significantly.

Muli-hop cellular network is a promising network architecture which incorporates the ad hoc characteristic into the cellular system aiming to improve current cellular network performance. Unlike single hop cellular network, due to involving autonomous devices in packet forwarding, routing process suffers from new security challenges which endanger the practical implementation of the network. One security challenge is that selfish devices do not relay other nodes' packets because cooperation consumes their resources and does not provide any immediate advantages. Selfish nodes degrade the network throughput, connectivity and power consumption. In order to stimulate the nodes' cooperation, we propose a micro-payment mechanism to reward the forwarding nodes and charge the communicating ones. The security analysis shows that the proposed mechanism is robust against rational attacks, and it can thwart some irrational ones. To evaluate the cost of applying our mechanism, an implementation model is proposed. The performance analysis based on the implementation model demonstrates that the overhead is acceptable.

Multi-hop cellular network is a promising architecture aiming to improve the performance of current cellular network. However, there are many security challenges due to the participation of the mobile nodes in the routing process. In this paper, we address two challenges: route anonymity, aiming to prevent attackers from tracking a packet flow to its source or destination; and location privacy, aiming to prevent attackers from detecting the nodes' locations. Most of the existing solutions require much computational power and energy. We propose a routing protocol that provides anonymous communication by protecting the user's anonymity and location privacy. The user's anonymity is preserved for a large number of compromised nodes. Simulations results show that the proposed protocol is efficient and can be implemented with an acceptable overhead.