Biblio

The problems of synthesis of a digital data encryption system based on dynamic chaos in a research project carried out at the Department of Marine Electronics (SMTU) are considered. A description is made of the problems of generating a chaotic (random) signal in computer systems with calculations with finite accuracy.

The incidence of abnormal road traffic events, especially abnormal traffic congestion, is becoming more and more prominent in daily traffic management in China. It has become the main research work of urban traffic management to detect and identify traffic congestion incidents in time. Efficient and accurate detection of traffic congestion incidents can provide a good strategy for traffic management. At present, the detection and recognition of traffic congestion events mainly rely on the integration of road traffic flow data and the passing data collected by electronic police or devices of checkpoint, and then estimating and forecasting road conditions through the method of big data analysis; Such methods often have some disadvantages such as low time-effect, low precision and small prediction range. Therefore, with the help of the current large and medium cities in the public security, traffic police have built video surveillance equipment, through computer vision technology to analyze the traffic flow from video monitoring, in this paper, the motion state and the changing trend of vehicle flow are obtained by using the technology of vehicle detection from video and multi-target tracking based on deep learning, so as to realize the perception and recognition of traffic congestion. The method achieves the recognition accuracy of less than 60 seconds in real-time, more than 80% in detection rate of congestion event and more than 82.5% in accuracy of detection. At the same time, it breaks through the restriction of traditional big data prediction, such as traffic flow data, truck pass data and GPS floating car data, and enlarges the scene and scope of detection.

Since trajectory data is widely collected and utilized for scientific research and business purpose, publishing trajectory without proper privacy-policy leads to an acute threat to individual data. Recently, several methods, i.e., k-anonymity, l-diversity, t-closeness have been studied, though they tend to protect by reducing data depends on a feature of each method. When a strong privacy protection is required, these methods have excessively reduced data utility that may affect the result of scientific research. In this research, we suggest a novel approach to tackle this existing dilemma via an adding noise trajectory on a vector-based grid environment.

Wireless networking opens up many opportunities to facilitate miniaturized robots in collaborative tasks, while the openness of wireless medium exposes robots to the threats of Sybil attackers, who can break the fundamental trust assumption in robotic collaboration by forging a large number of fictitious robots. Recent advances advocate the adoption of bulky multi-antenna systems to passively obtain fine-grained physical layer signatures, rendering them unaffordable to miniaturized robots. To overcome this conundrum, this paper presents ScatterID, a lightweight system that attaches featherlight and batteryless backscatter tags to single-antenna robots to defend against Sybil attacks. Instead of passively "observing" signatures, ScatterID actively "manipulates" multipath propagation by using backscatter tags to intentionally create rich multipath features obtainable to a single-antenna robot. These features are used to construct a distinct profile to detect the real signal source, even when the attacker is mobile and power-scaling. We implement ScatterID on the iRobot Create platform and evaluate it in typical indoor and outdoor environments. The experimental results show that our system achieves a high AUROC of 0.988 and an overall accuracy of 96.4% for identity verification.

Vehicular Ad Hoc Network (VANET) is a promising technology for autonomous driving as it provides many benefits and user conveniences to improve road safety and driving comfort. Sybil attack is one of the most serious threats in vehicular communications because attackers can generate multiple forged identities to disseminate false messages to disrupt safety-related services or misuse the systems. To address this issue, we propose a Sybil attack detection scheme using ADAS (Advanced Driving Assistant System) sensors installed on modern passenger vehicles, without the assistance of trusted third party authorities or infrastructure. Also, a deep learning based object detection technique is used to accurately identify nearby objects for Sybil attack detection and the multi-step verification process minimizes the false positive of the detection.

Vehicular Ad-hoc Networks (VANETs) is a traditional mobile ad hoc network (MANET) used on traffic roads and it is a special mobile ad hoc network. As an intelligent transportation system, VANETs can solve driving safety and provide value-added services. Therefore, the application of VANETs can improve the safety and efficiency of road traffic. Location services are in a crucial position for the development of VANETs. VANETs has the characteristics of open access and wireless communication. Malicious node attacks may lead to the leakage of user privacy in VANETs, thus seriously affecting the use of VANETs. Therefore, the location privacy issue of VANETs cannot be ignored. This paper classifies the attack methods in VANETs, and summarizes and compares the location privacy protection techniques proposed in the existing research.

The e-government concept and healthcare have usually been studied separately. Even when and where both e-government and healthcare systems were combined in a study, the roles of e-government in healthcare have not been examined. As a result., the complementarity of the systems poses potential challenges. The interpretive approach was applied in this study. Existing materials in the areas of healthcare and e-government were used as data from a qualitative method viewpoint. Dimension of change from the perspective of the structuration theory was employed to guide the data analysis. From the analysis., six factors were found to be the main roles of e-government in the implementation and application of e-health in the delivering of healthcare services. An understanding of the roles of e-government promotes complementarity., which enhances the healthcare service delivery to the community.

In this paper, we propose a compositional scheme for the construction of abstractions for networks of control systems by using the interconnection matrix and joint dissipativity-type properties of subsystems and their abstractions. In the proposed framework, the abstraction, itself a control system (possibly with a lower dimension), can be used as a substitution of the original system in the controller design process. Moreover, we provide a procedure for constructing abstractions of a class of nonlinear control systems by using the bounds on the slope of system nonlinearities. We illustrate the proposed results on a network of linear control systems by constructing its abstraction in a compositional way without requiring any condition on the number or gains of the subsystems. We use the abstraction as a substitute to synthesize a controller enforcing a certain linear temporal logic specification. This example particularly elucidates the effectiveness of dissipativity-type compositional reasoning for large-scale systems.

We consider a compositional construction of approximate abstractions of interconnected control systems. In our framework, an abstraction acts as a substitute in the controller design process and is itself a continuous control system. The abstraction is related to the concrete control system via a so-called simulation function: a Lyapunov-like function, which is used to establish a quantitative bound between the behavior of the approximate abstraction and the concrete system. In the first part of the paper, we provide a small gain type condition that facilitates the compositional construction of an abstraction of an interconnected control system together with a simulation function from the abstractions and simulation functions of the individual subsystems. In the second part of the paper, we restrict our attention to linear control system and characterize simulation functions in terms of controlled invariant, externally stabilizable subspaces. Based on those characterizations, we propose a particular scheme to construct abstractions for linear control systems. We illustrate the compositional construction of an abstraction on an interconnected system consisting of four linear subsystems. We use the abstraction as a substitute to synthesize a controller to enforce a certain linear temporal logic specification.

Information communicated within cyber-physical systems (CPSs) is often used in determining the physical states of such systems, and malicious adversaries may intercept these communications in order to infer future states of a CPS or its components. Accordingly, there arises a need to protect the state values of a system. Recently, the notion of differential privacy has been used to protect state trajectories in dynamical systems, and it is this notion of privacy that we use here to protect the state trajectories of CPSs. We incorporate a cloud computer to coordinate the agents comprising the CPSs of interest, and the cloud offers the ability to remotely coordinate many agents, rapidly perform computations, and broadcast the results, making it a natural fit for systems with many interacting agents or components. Striving for broad applicability, we solve infinite-horizon linear-quadratic-regulator (LQR) problems, and each agent protects its own state trajectory by adding noise to its states before they are sent to the cloud. The cloud then uses these state values to generate optimal inputs for the agents. As a result, private data are fed into feedback loops at each iteration, and each noisy term affects every future state of every agent. In this paper, we show that the differentially private LQR problem can be related to the well-studied linear-quadratic-Gaussian (LQG) problem, and we provide bounds on how agents' privacy requirements affect the cloud's ability to generate optimal feedback control values for the agents. These results are illustrated in numerical simulations.

Collaborative trajectory privacy preservation (CTPP) scheme is an effective method for continuous queries. However, collaborating with other users need pay some cost. Therefore, some rational and selfish users will not choose collaboration, which will result in users' privacy disclosing. To solve the problem, this paper proposes a collaboration incentive mechanism by rewarding collaborative users and punishing non-collaborative users. The paper models the interactions of users participating in CTPP as a repeated game and analysis the utility of participated users. The analytical results show that CTPP with the proposed incentive mechanism can maximize user's payoffs. Experiments show that the proposed mechanism can effectively encourage users' collaboration behavior and effectively preserve the trajectory privacy for continuous query users.

Control algorithms, like model predictive control, can be computationally expensive and may benefit from being executed over the cloud. This is especially the case for nodes at the edge of a network since they tend to have reduced computational capabilities. However, control over the cloud requires transmission of sensitive data (e.g., system dynamics, measurements) which undermines privacy of these nodes. When choosing a method to protect the privacy of these data, efficiency must be considered to the same extent as privacy guarantees to ensure adequate control performance. In this paper, we review a transformation-based method for protecting privacy, previously introduced by the authors, and quantify the level of privacy it provides. Moreover, we also consider the case of adversaries with side knowledge and quantify how much privacy is lost as a function of the side knowledge of the adversary.

This paper is concerned with the stabilization problem of cyber physical system (CPS) exposed to a random replay attack. The study will ignore the effects of communication delays and packet losses, and the attention will be focused on the effect of replay attack on the stability of (CPS). The closed-loop system is modeled as Markovian jump linear system with two jumping parameters. Linear matrix inequality (LMI) formulation is used to give a condition for stochastic stabilization of the system. Finally the theory is illustrated through a numerical example.

With the development of location technology, location-based services greatly facilitate people's life . However, due to the location information contains a large amount of user sensitive informations, the servicer in location-based services published location data also be subject to the risk of privacy disclosure. In particular, it is more easy to lead to privacy leaks without considering the attacker's semantic background knowledge while the publish sparse location data. So, we proposed semantic k-anonymity privacy protection method to against above problem in this paper. In this method, we first proposed multi-user compressing sensing method to reconstruct the missing location data . To balance the availability and privacy requirment of anonymity set, We use semantic translation and multi-view fusion to selected non-sensitive data to join anonymous set. Experiment results on two real world datasets demonstrate that our solution improve the quality of privacy protection to against semantic attacks.

With the development of cloud computing technology in the Internet of Things (IoT), the trajectory privacy in location-based services (LBSs) has attracted much attention. Most of the existing work adopts point-to-point and centralized models, which will bring a heavy burden to the user and cause performance bottlenecks. Moreover, previous schemes did not consider both online and offline trajectory protection and ignored some hidden background information. Therefore, in this paper, we design a trajectory protection scheme based on fog computing and k-anonymity for real-time trajectory privacy protection in continuous queries and offline trajectory data protection in trajectory publication. Fog computing provides the user with local storage and mobility to ensure physical control, and k-anonymity constructs the cloaking region for each snapshot in terms of time-dependent query probability and transition probability. In this way, two k-anonymity-based dummy generation algorithms are proposed, which achieve the maximum entropy of online and offline trajectory protection. Security analysis and simulation results indicate that our scheme can realize trajectory protection effectively and efficiently.

Due to the leakage of privacy information in the sensitive region of trajectory anonymity publishing, which is resulted by the attack, this paper aims at the trajectory anonymity algorithm of division of region. According to the start stop time of the trajectory, the current sensitive region is found with the k-anonymity set on the synchronous trajectory. If the distance between the divided sub-region and the adjacent anonymous area is not greater than the threshold d, the area will be combined. Otherwise, with the guidance of location mapping, the forged location is added to the sub-region according to the original location so that the divided sub-region can meet the principle of k-anonymity. While the forged location retains the relative position of each point in the sensitive region, making that the divided sub-region and the original Regional anonymity are consistent. Experiments show that compared with the existing trajectory anonymous algorithm and the synchronous trajectory data set with the same privacy, the algorithm is highly effective in both privacy protection and validity of data quality.

Generalized chaos systems have more complex dynamic behavior than conventional chaos systems. If a generalized response system can be synchronized with a conventional drive system, the flexible control parameters and unpredictable synchronization state will increase significantly. The study first constructs a four-dimensional nonlinear dynamic equation with quadratic variables as a drive system. The numerical simulation and analyses of the Lyapunov exponent show that it is also a chaotic system. Based on the generalized chaos synchronization (GCS) theory, a four-dimensional diffeomorphism function is designed, and the corresponding GCS response system is generated. Simultaneously, the structural and synchronous circuits of information interaction and control are constructed with Multisim™ software, with the circuit simulation resulting in a good agreement with the numerical calculations. In order to verify the practical effect of generalized synchronization, an RGB digital image secure communication scheme is proposed. We confuse a 24-bit true color image with the designed GCS system, extend the original image to 48-bits, analyze the scheme security from keyspace, key sensitivity and non-symmetric identity authentication, classical types of attacks, and statistical average from the histogram, image correlation. The research results show that this GCS system is simple and feasible, and the encryption algorithm is closely related to the confidential information, which can resist the differential attack. The scheme is suitable to be applied in network images or other multimedia safe communications.

Although transport agency has employed desensitization techniques to deal with the privacy information when publicizing vehicle license plate recognition (VLPR) data, the adversaries can still eavesdrop on vehicle trajectories by certain means and further acquire the associated person and vehicle information through background knowledge. In this work, a privacy attacking method by using the desensitized VLPR data is proposed to link the vehicle trajectory. First the road average speed is evaluated by analyzing the changes of traffic flow, which is used to estimate the vehicle's travel time to the next VLPR system. Then the vehicle suspicion list is constructed through the time relevance of neighboring VLPR systems. Finally, since vehicles may have the same features like color, type, etc, the target trajectory will be located by filtering the suspected list by the rule of qualified identifier (QI) attributes and closest time method. Based on the Foshan City's VLPR data, the method is tested and results show that correct vehicle trajectory can be linked, which proves that the current VLPR data publication way has the risk of privacy disclosure. At last, the effects of related parameters on the proposed method are discussed and effective suggestions are made for publicizing VLPR date in the future.

The CPS standard can be more objective to evaluate the effect of control behavior in each control area on the interconnected power grid. The CPS standard is derived from statistical methods emphasizing the long-term control performance of AGC, which is beneficial to the frequency control of the power grid by mutual support between the various power grids in the case of an accident. Moreover, CPS standard reduces the wear of the equipment caused by the frequent adjustment of the AGC unit. The key is to adjust the AGC control strategy to meet the performance of CPS standard. This paper proposed a dynamic optimal CPS control methodology for interconnected power systems based on model predictive control which can achieve optimal control under the premise of meeting the CPS standard. The effectiveness of the control strategy is verified by simulation examples.

Mobile payment has drawn considerable attention due to its convenience of paying via personal mobile devices at anytime and anywhere, and passcodes (i.e., PINs or patterns) are the first choice of most consumers to authorize the payment. This paper demonstrates a serious security breach and aims to raise the awareness of the public that the passcodes for authorizing transactions in mobile payments can be leaked by exploiting the embedded sensors in wearable devices (e.g., smartwatches). We present a passcode inference system, WristSpy, which examines to what extent the user's PIN/pattern during the mobile payment could be revealed from a single wrist-worn wearable device under different passcode input scenarios involving either two hands or a single hand. In particular, WristSpy has the capability to accurately reconstruct fine-grained hand movement trajectories and infer PINs/patterns when mobile and wearable devices are on two hands through building a Euclidean distance-based model and developing a training-free parallel PIN/pattern inference algorithm. When both devices are on the same single hand, a highly challenging case, WristSpy extracts multi-dimensional features by capturing the dynamics of minute hand vibrations and performs machine-learning based classification to identify PIN entries. Extensive experiments with 15 volunteers and 1600 passcode inputs demonstrate that an adversary is able to recover a user's PIN/pattern with up to 92% success rate within 5 tries under various input scenarios.

In mobile wireless sensor networks (MWSN), data imprecision is a common problem. Decision making in real time applications may be greatly affected by a minor error. Even though there are many existing techniques that take advantage of the spatio-temporal characteristics exhibited in mobile environments, few measure the trustworthiness of sensor data accuracy. We propose a unique online context-aware data cleaning method that measures trustworthiness by employing an initial candidate reduction through the analysis of trust parameters used in financial markets theory. Sensors with similar trajectory behaviors are assigned trust scores estimated through the calculation of “betas” for finding the most accurate data to trust. Instead of devoting all the trust into a single candidate sensor's data to perform the cleaning, a Diversified Trust Portfolio (DTP) is generated based on the selected set of spatially autocorrelated candidate sensors. Our results show that samples cleaned by the proposed method exhibit lower percent error when compared to two well-known and effective data cleaning algorithms in tested outdoor and indoor scenarios.

Ubiquitous deployment of low-cost mobile positioning devices and the widespread use of high-speed wireless networks enable massive collection of large-scale trajectory data of individuals moving on road networks. Trajectory data mining finds numerous applications including understanding users' historical travel preferences and recommending places of interest to new visitors. Privacy-preserving trajectory mining is an important and challenging problem as exposure of sensitive location information in the trajectories can directly invade the location privacy of the users associated with the trajectories. In this paper, we propose a differentially private trajectory analysis algorithm for points-of-interest recommendation to users that aims at maximizing the accuracy of the recommendation results while protecting the privacy of the exposed trajectories with differential privacy guarantees. Our algorithm first transforms the raw trajectory dataset into a bipartite graph with nodes representing the users and the points-of-interest and the edges representing the visits made by the users to the locations, and then extracts the association matrix representing the bipartite graph to inject carefully calibrated noise to meet έ-differential privacy guarantees. A post-processing of the perturbed association matrix is performed to suppress noise prior to performing a Hyperlink-Induced Topic Search (HITS) on the transformed data that generates an ordered list of recommended points-of-interest. Extensive experiments on a real trajectory dataset show that our algorithm is efficient, scalable and demonstrates high recommendation accuracy while meeting the required differential privacy guarantees.

A visible nearest neighbor (VNN) query returns the k nearest objects that are visible to a query point, which is used to support various applications such as route planning, target monitoring, and antenna placement. However, with the proliferation of wireless communications and advances in positioning technology for mobile equipments, efficiently searching for VNN among moving objects are required. While most previous work on VNN query focused on static objects, in this paper, we treats the objects as moving consecutively when indexing them, and study the visible nearest neighbor query for moving objects (MVNN) . Assuming that the objects are represented as trajectories given by linear functions of time, we propose a scheme which indexes the moving objects by time-parameterized R-tree (TPR-tree) and obstacles by R-tree. The paper offers four heuristics for visibility and space pruning. New algorithms, Post-pruning and United-pruning, are developed for efficiently solving MVNN queries with all four heuristics. The effectiveness and efficiency of our solutions are verified by extensive experiments over synthetic datasets on real road network.

This work introduces concepts and algorithms along with a case study validating them, to enhance the event detection, pattern recognition and anomaly identification results in real life video surveillance. The motivation for the work underlies in the observation that human behavioral patterns in general continuously evolve and adapt with time, rather than being static. First, limitations in existing work with respect to this phenomena are identified. Accordingly, the notion and algorithms of Dynamic Clustering are introduced in order to overcome these drawbacks. Correspondingly, we propose the concept of maintaining two separate sets of data in parallel, namely the Normal Plane and the Anomaly Plane, to successfully achieve the task of learning continuously. The practicability of the proposed algorithms in a real life scenario is demonstrated through a case study. From the analysis presented in this work, it is evident that a more comprehensive analysis, closely following human perception can be accomplished by incorporating the proposed notions and algorithms in a video surveillance event.

In this paper, the mathematical framework of behavioral system will be applied to detect the cyber-attack on the networked control system which is used to control the remotely operated underwater vehicle ROV. The Intelligent Generalized Predictive Controller IGPC is used to control the ROV. The IGPC is designed with fault-tolerant ability. In consequence of the used fault accommodation technique, the proposed cyber-attacks detector is able to clearly detect the presence of attacker control signal and to distinguish between the effects of the attacker signal and fault on the plant side. The test result of the suggested method demonstrates that it can be considerably used for detection of the cyber-attack.