Biblio
The purpose of the General Data Protection Regulation (GDPR) is to provide improved privacy protection. If an app controls personal data from users, it needs to be compliant with GDPR. However, GDPR lists general rules rather than exact step-by-step guidelines about how to develop an app that fulfills the requirements. Therefore, there may exist GDPR compliance violations in existing apps, which would pose severe privacy threats to app users. In this paper, we take mobile health applications (mHealth apps) as a peephole to examine the status quo of GDPR compliance in Android apps. We first propose an automated system, named HPDROID, to bridge the semantic gap between the general rules of GDPR and the app implementations by identifying the data practices declared in the app privacy policy and the data relevant behaviors in the app code. Then, based on HPDROID, we detect three kinds of GDPR compliance violations, including the incompleteness of privacy policy, the inconsistency of data collections, and the insecurity of data transmission. We perform an empirical evaluation of 796 mHealth apps. The results reveal that 189 (23.7%) of them do not provide complete privacy policies. Moreover, 59 apps collect sensitive data through different measures, but 46 (77.9%) of them contain at least one inconsistent collection behavior. Even worse, among the 59 apps, only 8 apps try to ensure the transmission security of collected data. However, all of them contain at least one encryption or SSL misuse. Our work exposes severe privacy issues to raise awareness of privacy protection for app users and developers.
Searchable encryption server protects privacal data of data owner from leaks. This paper analyzes the security of a multi-user searchable encryption scheme and points out that this scheme does not satisfy the invisibility of trapdoors. In order to improve the security of the original scheme, this paper proposes a probably secure multi-user multi-keyword searchable encryption scheme. New secheme not only ensures the confidentiality of the cipher text keyword, but also does not increase the encryption workload of the data owner when the new data user joins. In the random oracle model, based on the hard problem of decisional Diffie-Hellman, it is proved that the scheme has trapdoor indistinguishability. In the end, obtained by the simulation program to achieve a new computationally efficient communication at low cost.
This paper presents a sequence switching control (SSC) scheme for buck converters with a series-inductor auxiliary circuit, aiming at improving the load transient response. During an unloading transient, the series inductor is controlled as a small equivalent inductance so as to achieve a fast transient regulation. While in the steady state, the series inductor behaves as a large inductance to reduce the output current ripple. Furthermore, on the basis of the proposed variable inductance circuit, a SSC control scheme is proposed and implemented in a digital form. With the proposed control scheme the unloading transient event is divided into n+1 sub-periods, and in each sub-period, the capacitor-charge balance principle is used to determine the switching time sequence. Furthermore, its feasibility is validated in experiment with a 12V-3.3V low-voltage high-current synchronous buck converter. Experimental results demonstrate that the voltage overshoot of the proposed SSC scheme has improved more than 74% compared to that of the time-optimal control (TOC) scheme.
Aiming at the phenomenon that the urban traffic is complex at present, the optimization algorithm of the traditional logistic distribution path isn't sensitive to the change of road condition without strong application in the actual logistics distribution, the optimization algorithm research of logistics distribution path based on the deep belief network is raised. Firstly, build the traffic forecast model based on the deep belief network, complete the model training and conduct the verification by learning lots of traffic data. On such basis, combine the predicated road condition with the traffic network to build the time-share traffic network, amend the access set and the pheromone variable of ant algorithm in accordance with the time-share traffic network, and raise the optimization algorithm of logistics distribution path based on the traffic forecasting. Finally, verify the superiority and application value of the algorithm in the actual distribution through the optimization algorithm contrast test with other logistics distribution paths.
In this paper, we study trust-related human factors in supervisory control of swarm robots with varied levels of autonomy (LOA) in a target foraging task. We compare three LOAs: manual, mixed-initiative (MI), and fully autonomous LOA. In the manual LOA, the human operator chooses headings for a flocking swarm, issuing new headings as needed. In the fully autonomous LOA, the swarm is redirected automatically by changing headings using a search algorithm. In the mixed-initiative LOA, if performance declines, control is switched from human to swarm or swarm to human. The result of this work extends the current knowledge on human factors in swarm supervisory control. Specifically, the finding that the relationship between trust and performance improved for passively monitoring operators (i.e., improved situation awareness in higher LOAs) is particularly novel in its contradiction of earlier work. We also discover that operators switch the degree of autonomy when their trust in the swarm system is low. Last, our analysis shows that operator's preference for a lower LOA is confirmed for a new domain of swarm control.
The monitoring circuit is widely applied in radiation environment and it is of significance to study the circuit reliability with the radiation effects. In this paper, an intelligent analysis method based on Deep Belief Network (DBN) and Support Vector Method is proposed according to the radiation experiments analysis of the monitoring circuit. The Total Ionizing Dose (TID) of the monitoring circuit is used to identify the circuit degradation trend. Firstly, the output waveforms of the monitoring circuit are obtained by radiating with the different TID. Subsequently, the Deep Belief Network Model is trained to extract the features of the circuit signal. Finally, the Support Vector Machine (SVM) and Support Vector Regression (SVR) are applied to classify and predict the remaining useful life (RUL) of the monitoring circuit. According to the experimental results, the performance of DBN-SVM exceeds DBN method for feature extraction and classification, and SVR is effective for predicting the degradation.
This Innovate Practice Work in Progress paper is about education on Cybersecurity, which is essential in training of innovative talents in the era of the Internet. Besides knowledge and skills, it is important as well to enhance the students' awareness of cybersecurity in daily life. Considering that contactless smart cards are common and widely used in various areas, one basic and two advanced contactless smart card experiments were designed innovatively and assigned to junior students in 3-people groups in an introductory cybersecurity summer course. The experimental principles, facilities, contents and arrangement are introduced successively. Classroom tests were managed before and after the experiments, and a box and whisker plot is used to describe the distributions of the scores in both tests. The experimental output and student feedback implied the learning objectives were achieved through the problem-based, active and group learning experience during the experiments.
With the globalization of integrated circuit design and manufacturing, Hardware Trojan have posed serious threats to the security of commercial chips. In this paper, we propose the framework of two-level temperature difference based thermal map analysis detection method. In our proposed method, thermal maps of an operating chip during a period are captured, and they are differentiated with the thermal maps of a golden model. Then every pixel's differential temperature of differential thermal maps is extracted and compared with other pixel's. To mitigate the Gaussian white noise and to differentiate the information of Hardware Trojan from the information of normal circuits, Kalman filter algorithm is involved. In our experiment, FPGAs configured with equivalent circuits are utilized to simulate the real chips to validate our proposed approach. The experimental result reveals that our proposed framework can detect Hardware Trojan whose power proportion magnitude is 10''3.
To improve the resilience of state estimation strategy against cyber attacks, the Compressive Sensing (CS) is applied in reconstruction of incomplete measurements for cyber physical systems. First, observability analysis is used to decide the time to run the reconstruction and the damage level from attacks. In particular, the dictionary learning is proposed to form the over-completed dictionary by K-Singular Value Decomposition (K-SVD). Besides, due to the irregularity of incomplete measurements, sampling matrix is designed as the measurement matrix. Finally, the simulation experiments on 6-bus power system illustrate that the proposed method achieves the incomplete measurements reconstruction perfectly, which is better than the joint dictionary. When only 29% available measurements are left, the proposed method has generality for four kinds of recovery algorithms.
The prevalence and effectiveness of phishing attacks, despite the presence of a vast array of technical defences, are due largely to the fact that attackers are ruthlessly targeting what is often referred to as the weakest link in the system - the human. This paper reports the results of an investigation into how end users behave when faced with phishing websites and how this behaviour exposes them to attack. Specifically, the paper presents a proof of concept computer model for simulating human behaviour with respect to phishing website detection based on the ACT-R cognitive architecture, and draws conclusions as to the applicability of this architecture to human behaviour modelling within a phishing detection scenario. Following the development of a high-level conceptual model of the phishing website detection process, the study draws upon ACT-R to model and simulate the cognitive processes involved in judging the validity of a representative webpage based primarily around the characteristics of the HTTPS padlock security indicator. The study concludes that despite the low-level nature of the architecture and its very basic user interface support, ACT-R possesses strong capabilities which map well onto the phishing use case, and that further work to more fully represent the range of human security knowledge and behaviours in an ACT-R model could lead to improved insights into how best to combine technical and human defences to reduce the risk to end users from phishing attacks.
Block recursive least square (BRLS) algorithm for dictionary learning in compressed sensing system is developed for surveillance video processing. The new method uses image blocks directly and iteratively to train dictionaries via BRLS algorithm, which is different from classical methods that require to transform blocks to columns first and then giving all training blocks at one time. Since the background in surveillance video is almost fixed, the residual of foreground can be represented sparsely and reconstructed with background subtraction directly. The new method and framework are applied in real image and surveillance video processing. Simulation results show that the new method achieves better representation performance than classical ones in both image and surveillance video.
Trying to solve the risk of data privacy disclosure in classification process, a Random Forest algorithm under differential privacy named DPRF-gini is proposed in the paper. In the process of building decision tree, the algorithm first disturbed the process of feature selection and attribute partition by using exponential mechanism, and then meet the requirement of differential privacy by adding Laplace noise to the leaf node. Compared with the original algorithm, Empirical results show that protection of data privacy is further enhanced while the accuracy of the algorithm is slightly reduced.