Information Flow Control Research Articles

Ensuring Cybersecurity of Digital Production Using Modern Neural Network Methods

The transition from the information economy to the digital one presents new challenges for society associated with the development of disruptive technologies, a network of cyber-physical systems, artificial intelligence and big data. When creating digital platforms, a number of difficulties arise: the large size of the digital infrastructure and its heterogeneity, poorly established information interaction between segments, the lack of a unified approach to ensuring cybersecurity and a high dependence on the qualifications of personnel and equipment reliability. The introduction of the digital economy leads to an increase in the risk of cyber threats associated with access control problems between information flow regulation and control systems. To solve the problems of detecting cyber threats, it is proposed to use generative adversarial neural networks. Algorithms for learning and testing a neural network were presented. The results of the experiments have demonstrated that the proposed solution is highly accurate in detecting cyberattacks.

Casting Conference Calls

We explore a subtle but important mechanism through which firms can control information flow to the markets. We find that firms that “cast” their conference calls by disproportionately calling on bullish analysts tend to underperform in the future. Firms that call on more favorable analysts experience more negative future earnings surprises and more future earnings restatements. A long–short portfolio that exploits this differential firm behavior earns abnormal returns of up to 149 basis points per month or almost 18% per year. We find similar evidence in an international sample of earnings call transcripts from the United Kingdom, Canada, France, and Japan. Firms with higher discretionary accruals, firms that barely meet/exceed earnings expectations, and firms (and their executives) that are about to issue equity, sell shares, and exercise options are all significantly more likely to cast their earnings calls.This paper was accepted by Tyler Shumway, finance.

Cell shape regulates subcellular organelle location to control early Ca2+ signal dynamics in vascular smooth muscle cells

The shape of the cell is connected to its function; however, we do not fully understand underlying mechanisms by which global shape regulates a cell’s functional capabilities. Using theory, experiments and simulation, we investigated how physiologically relevant cell shape changes affect subcellular organization, and consequently intracellular signaling, to control information flow needed for phenotypic function. Vascular smooth muscle cells going from a proliferative and motile circular shape to a contractile fusiform shape show changes in the location of the sarcoplasmic reticulum, inter-organelle distances, and differential distribution of receptors in the plasma membrane. These factors together lead to the modulation of signals transduced by the M3 muscarinic receptor/Gq/PLCβ pathway at the plasma membrane, amplifying Ca2+ dynamics in the cytoplasm, and the nucleus resulting in phenotypic changes, as determined by increased activity of myosin light chain kinase in the cytoplasm and enhanced nuclear localization of the transcription factor NFAT. Taken together, our observations show a systems level phenomenon whereby global cell shape affects subcellular organization to modulate signaling that enables phenotypic changes.

Exploring Crisis Communication and Information Dissemination on Social Media: Social Network Analysis of Hurricane Irma Tweets

This study utilized social network analysis to identify the top 10 Twitter influentials during the Hurricane Irma crisis period and examined the relationship between social media attributes and the bridge influence of controlling information flow. The number of a user’s followers and tweets significantly predicted one’s control of information. Crisis information tended to be shared in scattered subgroups. Social network boundaries impeded information diffusion, and the communication pattern was largely one-way. The findings partially supported the opinion leader argument while indicating that influentials can directly generate information, which is consistent with the social-mediated crisis communication model. Such findings will contribute to crisis literature and help emergency management professionals advance social media usage to disseminate crisis information, build effective communication, and provide immediate disaster relief responses

Gated dynamic convolutions with deep layer fusion for abstractive document summarization

We present a novel abstractive document summarization based on the recently proposed dynamic convolutional encoder-decoder architectures. We address several aspects of summarization that are not well modeled by the basic architecture, by integrating multiple layers of the encoder, controlling information flow in the hierarchy, and exploiting external knowledge. First, we propose a simple and efficient deep layer fusion to extract salient information from the encoder layers. Second, we propose a gating mechanism to control and maintain important contextual information through the encoder-decoder layers into dynamic convolutions. Lastly, we put part-of-speech information into the model as external knowledge to better predict filters for dynamic convolutions. We evaluate our model using ROUGE metrics on three different datasets: CNN-DM, NEWSROOM-ABS, and XSUM. Experimental results show that the proposed model outperforms the state-of-the-art abstractive models on NEWSROOM-ABS and XSUM and shows comparable scores on CNN-DM.

Access control encryption without sanitizers for Internet of Energy

With the increasing popularity of Internet of Energy (IoE), more and more physical devices connected to IoE depend on the information system for energy control and coordination. Under this condition, how to achieve information flow control in IoE becomes a great challenge. Access control encryption (ACE) is a promising technology to address the problem. However, existing ACE requires a centralized sanitizer, hindering its successful application in IoE. In this paper, we construct a new kind of ACE without sanitizers for IoE. We first construct a basic ACE scheme based on number-theoretic assumptions (i.e., DBDH assumption), and this scheme can control not only what users can read but also what they can write. To resist against the quantum attacks, we further construct a more secure ACE scheme based on learning with errors (LWE). We formally prove that our proposed two ACE schemes are secure under the proposed security definition, and we also evaluate the applicability and the efficiency of them in experiments.

Critical Roles of Embryonic Born Dorsal Dentate Granule Neurons for Activity-Dependent Increases in BDNF, Adult Hippocampal Neurogenesis, and Antianxiety-like Behaviors

BackgroundDentate gyrus (DG), a “gate” that controls information flow into the hippocampus, plays important roles in regulating both cognitive (e.g., spatial learning and memory) and mood behaviors. Deficits in DG neurons contribute to the pathogenesis of not only neurological, but also psychiatric, disorders, such as anxiety disorder. Whereas DG’s function in spatial learning and memory has been extensively investigated, its role in regulating anxiety remains elusive. MethodsUsing c-Fos to mark DG neuron activation, we identified a group of embryonic born dorsal DG (dDG) neurons, which were activated by anxiogenic stimuli and specifically express osteocalcin (Ocn)-Cre. We further investigated their functions in regulating anxiety and the underlying mechanisms by using a combination of chemogenetic, electrophysiological, and RNA-sequencing methods. ResultsThe Ocn-Cre+ dDG neurons were highly active in response to anxiogenic environment but had lower excitability and fewer presynaptic inputs than those of Ocn-Cre− or adult born dDG neurons. Activating Ocn-Cre+ dDG neurons suppressed anxiety-like behaviors and increased adult DG neurogenesis, whereas ablating or chronically inhibiting Ocn-Cre+ dDG neurons exacerbated anxiety-like behaviors, impaired adult DG neurogenesis, and abolished activity (e.g., voluntary wheel running)-induced anxiolytic effect and adult DG neurogenesis. RNA-sequencing screening for factors induced by activation of Ocn-Cre+ dDG neurons identified BDNF, which was required for Ocn-Cre+ dDG neurons mediated antianxiety-like behaviors and adult DG neurogenesis. ConclusionsThese results demonstrate critical functions of Ocn-Cre+ dDG neurons in suppressing anxiety-like behaviors but promoting adult DG neurogenesis, and both functions are likely through activation of BDNF.

Handling Missing Sensors in Topology-Aware IoT Applications with Gated Graph Neural Network

Reliable data collection, transmission, and delivery on Internet of Things (IoT) systems is crucial in order to provide high-quality intelligent services. However, sensor data delivery can be interrupted for various reasons, such as sensor malfunction, network failures, and external attacks. Thus, only data from a partial set of sensors may be available. We call it the missing sensor problem. This problem can lead to severe performance degradation at inference time by neural-network-based recognition models trained on the complete sensor set. This paper enhances the robustness of neural network models to the missing sensor problem by introducing a novel feature reconstruction module, named the graph recovery module, that handles missing sensors directly inside the network. Specifically, we consider topology-aware IoT applications, where sensors are placed on a physically interconnected network. We design a novel neural message passing mechanism that logically mimics physical network topology, based on recent advances in graph neural networks (GNNs). We rely on a spatial locality assumption, where only correlations between physically connected sensors are explicitly explored. When encountering missing sensors, information is passed from available sensors to missing sensors to be used to reconstruct their features. Moreover, at each message passing step, we utilize a gating mechanism inspired by Gated Recurrent Units (GRUs) to automatically control information flow between available sensors and missing sensors. We empirically evaluate the reconstruction performance of the graph recovery module with two representative IoT applications; human activity recognition (HAR) and electroencephalogram (EEG)-based motor-imagery classification, on three public datasets. Two different backbone networks are utilized for the tasks. Our design is shown to effectively maintain model performance, suffering only 7% to 18% accuracy loss when as much as 90% of sensors are removed, compared to a drop of 15% to 47% in the accuracy of competing state-of-the-art algorithms under the same conditions. The accuracy gap is largest when more sensors are missing.

Liquid information flow control

We present Lifty, a domain-specific language for data-centric applications that manipulate sensitive data. A Lifty programmer annotates the sources of sensitive data with declarative security policies, and the language statically and automatically verifies that the application handles the data according to the policies. Moreover, if verification fails, Lifty suggests a provably correct repair, thereby easing the programmer burden of implementing policy enforcing code throughout the application.The main insight behind Lifty is to encode information flow control using liquid types, an expressive yet decidable type system. Liquid types enable fully automatic checking of complex, data dependent policies, and power our repair mechanism via type-driven error localization and patch synthesis. Our experience using Lifty to implement three case studies from the literature shows that (1) the Lifty policy language is sufficiently expressive to specify many real-world policies, (2) the Lifty type checker is able to verify secure programs and find leaks in insecure programs quickly, and (3) even if the programmer leaves out all policy enforcing code, the Lifty repair engine is able to patch all leaks automatically within a reasonable time.

Exploring the effect of context information on deep learning business process predictions

ABSTRACT Predictive Process Monitoring (PPM) techniques for predicting the next activity in running business processes developed into an established topic of Business Process Management. Recent research suggests using Deep Neural Networks (DNNs) for PPM because DNNs are good at learning the intricate structure of business processes. Most of these works use Long Short-Term Memory Neural Networks (LSTMs) and consider only the control flow information of an event log. Beyond control flow information, context information can add valuable information to a predictive model. However, the effects of context attributes on the predictive quality have not yet been sufficiently analyzed. This work addresses this gap and provides two insights. First, a context-sensitive prediction capability can improve the predictive quality of an LSTM-based technique. Second, the added value of context information to the quality of predicting the next activity varies in the course of a running process instance.

STRATEGIES AND OPERATION PRINCIPLES OF LOGISTIC INFORMATION SYSTEMS

Over the last few years the so-called new logistic technologies are rapidly developed. Information systems hold the central position in these technologies. Development of logistics in the developed countries not least are stimulated with the need of fast reaction of producers to market condition, aspiration to adapt in the changing situations in short time. The central idea of logistics is planning, management and control of entrepreneurial activity, all material and information flows connected with this activity. Information systems in logistics assume fast appropriate response to the market demand, tracking delivery time, optimization of functions in chains of delivery and supply. The research of modern backbone of information technologies in logistics is conducted in this article. Modern information technologies in logistics are analyzed. Modern technologies of information data processing in logistic streams and creation of logistic information technologies are also considered in the article. Some types of information technologies in logistics, structural principles and principles of operation of logistic information systems and also problems in the process of creation of logistic information technologies are described. The article is addressed to a wide range of researchers and experts in the field of logistics and information technologies.

N-Back Task Training Helps to Improve Post-error Performance.

Improved performance on working memory (WM) through training has been widely expected to transfer to other domains. Recent studies have proposed that WM training could enhance the autonomous coordination of WM processes. Based on the shared processes between WM and error processing, our present study explored the transfer effect of 15 days of training on post-error performance, during the n-back task, compared to a simple visual search task. Participants were randomly assigned to either the training (N = 22) or control (N = 18) group. We found that WM training successfully improved WM performance. After training, compared with the control group, the training group showed a significant reduction in post-error slowing (PES); however, post-error accuracy and the flanker effect were not modulated by WM training. Moreover, we observed a significant, negative correlation between the changes in PES and WM from pretest to posttest and classified two groups based on these changes in PES with 70% accuracy. Thus, in our present sample, WM training improved post-error performance. We propose that the skill of controlling information flow, developed during WM training, is transferable to other tasks and discuss the implications of current findings for understanding the generation of PES.

The functioning of information system in the society of the Second modern in conditions of military conflict

The features of functioning of information system in the society of the Second modern in conditions of military conflict are analyzed in the article. Also we tried to analyze connection of this features with characteristics of the political and economic systems of this type of society. Television continues to be the main source of information about state of affairs in Ukraine and in the world, although it’s influence is decreasing. The concentration of media ownership in the hands of financial and industrial groups, associated with political forces, is an important issue. Online media and social networks are the second among the sources of information about state of affairs in Ukraine and in the world. Their increasing influence raises the question of the power of large internet companies, who have the ability to control information flows, provide an opportunity to use the information aggregated by them for the application of specific political technologies of influence on the public sphere. These companies are out of control of the regulatory mechanisms of state institutions in most countries, which creates vulnerabilities in the public sphere of nation-states to influence from other countries and unregulated aspects of online electoral campaigns. The crisis of confidence in traditional media increases the importance of offline and online networks of social interactions as a source of information. Data in Ukraine, Europe and USA show that loss of confidence in public institutions, rise of populism directly related to the decline in confidence in traditional media. The level of trust in vaccination in different regions of the world is also analyzed in the context of the functioning of media institutions and other public institutions. Modern media (both traditional and internet) tend to mix entertaining formats with political information. Converting policy to show, spreading fakes, noticeable dependence of media on certain political and economic groups and media’s partiality, weakening of expert filters undermines confidence in traditional and new media. Although the importance of social media for the democratization of the public sphere exists. Decreasing confidence in media in general converted to the сonfidence in concrete media figures (bloggers, experts etc.). The article also contains generalization of researches of media consumption in Ukraine in first part of 2019.

Efficient monitoring of hyperproperties using prefix trees

Hyperproperties, such as non-interference and observational determinism, relate multiple computation traces with each other and are thus not monitorable by tools that consider computations in isolation. We present the monitoring approach implemented in the latest version of text {RVHyper}, a runtime verification tool for hyperproperties. The input to the tool are specifications given in the temporal logic text {HyperLTL}, which extends linear-time temporal logic (LTL) with trace quantifiers and trace variables. text {RVHyper} processes execution traces sequentially until a violation of the specification is detected. In this case, a counterexample, in the form of a set of traces, is returned. text {RVHyper} employs a range of optimizations: a preprocessing analysis of the specification and a procedure that minimizes the traces that need to be stored during the monitoring process. In this article, we introduce a novel trace storage technique that arranges the traces in a tree-like structure to exploit partially equal traces. We evaluate text {RVHyper} on existing benchmarks on secure information flow control, error correcting codes, and symmetry in hardware designs. As an example application outside of security, we show how text {RVHyper} can be used to detect spurious dependencies in hardware designs.

On the expressiveness and semantics of information flow types

Information Flow Control (IFC) is a form of dependence analysis that tracks and prohibits dependence of public outputs on secret inputs. Such a dependence analysis is often carried out using a type system. IFC type systems can track dependence (via confidentiality labels) at varying levels of granu larity. On one extreme, there are fine-grained type systems that track dependence at the level of individual values. They label individual values. On the other extreme, there are coarse-grained type systems that track dependence at the level of entire computations. These type systems do not label individual values but instead label entire sub-computations. An important foundational question is one of the relative expressiveness of these two classes of IFC type systems. In this paper we show that, despite the glaring differences in how they track dependence, the two classes of type systems are actually equally expressive. We do this by showing translations from FG, a fine-grained IFC type system derived from SLAM (In Proceedings of the ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL) (1998)), to SLIO∗, a coarse-grained IFC type system derived from HLIO (In Proceedings of the ACM SIGPLAN International Conference on Functional Programming (ICFP) (2015)), and vice-versa. The translation from SLIO∗ to FG is straightforward since FG tracks dependence at a granularity finer than SLIO∗ does. However, the translation from FG to SLIO∗ is quite involved and relies extensively on label quantification. We further examine the reason for this complexity using a slight variant of SLIO∗, called CG, to which FG can be translated more easily. As a separate, more foundational contribution we show how to extend logical relation models of information flow to languages with higher-order state. Specifically, we build world-indexed (Kripke) logical relations for FG, SLIO∗ and CG, which we use to prove these type systems sound and also to prove the translations between them correct.

Information control in networked discrete event systems and its application to battery management systems

Opacity is an important property in control of information flow among networked agents. In this paper, we investigate information control problems in networked discrete event systems using opacity. In a networked discrete event system, communication among agents is via a shared communication network. Since delays and losses are unavoidable in networked discrete event systems, they must be considered in investigating opacity. We call opacity under communication delays and losses network opacity. We first define three network opacities: strong network opacity, weak network opacity, and network non-opacity. We derive necessary and sufficient conditions for network opacities and develop methods to check network opacities. We then apply network opacity to solve a problem in battery management systems.

Control Reliability Analysis of Distributed Storages in Energy Internet

As an important part of the Energy Internet, distributed storage energy needs to consider the comprehensive effectiveness of control flow and information flow. This article introduces that the information flow is from the energy distributed energy storage control point to the intercommunion point, and then to the energy distributed energy storage control point. On the basis of the flow-distributed energy storage control flow coupling model, the Bayesian conditional probability network theory is used to give the Energy Internet distributed energy storage controllability measurement under the condition of intercommunion interruption. This paper detects the impact of information transmission on distributed energy storage reliability by setting up an interrupt signal during information transmission. This modal is of great significance for the control of distributed energy storage.

Enhance the security properties and information flow control

Enhance the security properties and information flow control

Enhance the security properties and information flow control

The fact, that users are connected from different devices to cloud computing and storage services via some particular authentication mechanisms. However, when it comes to credential information such as the login and password, they are most likely to be intrusively utilised without necessarily been detected. Hence, the security policy of cloud computing has to manage both access control and viral analysis so that to guarantee security properties of tenants and organisations. The main purpose of this paper is to enhance the security properties by formulating them in a dynamic way through analysing the behaviour of entities and associate them with a trust level and security class. For this reason, we have implemented a security policy which the main role is to create a template, in order to guarantee the security properties namely the confidentiality, integrity and availability (CIA). Our results obtained during our experiments show the efficiency of our approach in terms of the classification and the real-time detection rate which reaches up to 95%.

Dynamic Control Method for Tenants’ Sensitive Information Flow Based on Virtual Boundary Recognition

In the cloud environment, owing to the large-scale sharing of the upper application instance and the underlying virtual machine resources, the tenants' information flow boundary in the shared virtual machine is fuzzy and difficult to identify. In addition, protection of tenant information flow between processes is inadequate, resulting in the leakage of sensitive information of tenants. Therefore, a dynamic control method for tenants' sensitive information flow based on virtual boundary recognition is proposed. By analyzing the behavior and operation log of tenants, the behavior feature vectors of tenants are constructed, and an automatic recognition algorithm of tenant virtual boundary based on the dynamic spiking neural network is designed. This algorithm can realize dynamic identification of the tenant virtual security boundary when the application service demand changes dynamically. Further, combined with the concept of centralized and decentralized information flow control, a dynamic control method of sensitive information flow is established. The security label is formally defined by using the lattice structure, and the control rules of tenants' information flow and the rules of tenant label encryption-declassification are designed. Thus, the independent, dynamic and secure control of tenants' information flow inside and outside the tenant virtual boundary. Finally, the detailed design of a dynamic security control application system for cloud tenants' sensitive information flow is provided. Experiments confirm that the proposed algorithm can identify the security boundary of tenants more accurately and efficiently than the traditional spiking neural network classification methods. Further, the security and effectiveness of the method is verified by the intransitive noninterference theory and the experiment of information flow control.