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2020-01-27
Tuba, Eva, Jovanovic, Raka, Zivkovic, Dejan, Beko, Marko, Tuba, Milan.  2019.  Clustering Algorithm Optimized by Brain Storm Optimization for Digital Image Segmentation. 2019 7th International Symposium on Digital Forensics and Security (ISDFS). :1–6.
In the last several decades digital images were extend their usage in numerous areas. Due to various digital image processing methods they became part areas such as astronomy, agriculture and more. One of the main task in image processing application is segmentation. Since segmentation represents rather important problem, various methods were proposed in the past. One of the methods is to use clustering algorithms which is explored in this paper. We propose k-means algorithm for digital image segmentation. K-means algorithm's well known drawback is the high possibility of getting trapped into local optima. In this paper we proposed brain storm optimization algorithm for optimizing k-means algorithm used for digital image segmentation. Our proposed algorithm is tested on several benchmark images and the results are compared with other stat-of-the-art algorithms. The proposed method outperformed the existing methods.
Qureshi, Ayyaz-Ul-Haq, Larijani, Hadi, Javed, Abbas, Mtetwa, Nhamoinesu, Ahmad, Jawad.  2019.  Intrusion Detection Using Swarm Intelligence. 2019 UK/ China Emerging Technologies (UCET). :1–5.
Recent advances in networking and communication technologies have enabled Internet-of-Things (IoT) devices to communicate more frequently and faster. An IoT device typically transmits data over the Internet which is an insecure channel. Cyber attacks such as denial-of-service (DoS), man-in-middle, and SQL injection are considered as big threats to IoT devices. In this paper, an anomaly-based intrusion detection scheme is proposed that can protect sensitive information and detect novel cyber-attacks. The Artificial Bee Colony (ABC) algorithm is used to train the Random Neural Network (RNN) based system (RNN-ABC). The proposed scheme is trained on NSL-KDD Train+ and tested for unseen data. The experimental results suggest that swarm intelligence and RNN successfully classify novel attacks with an accuracy of 91.65%. Additionally, the performance of the proposed scheme is also compared with a hybrid multilayer perceptron (MLP) based intrusion detection system using sensitivity, mean of mean squared error (MMSE), the standard deviation of MSE (SDMSE), best mean squared error (BMSE) and worst mean squared error (WMSE) parameters. All experimental tests confirm the robustness and high accuracy of the proposed scheme.
Kala, T. Sree, Christy, A..  2019.  An Intrusion Detection System using Opposition based Particle Swarm Optimization Algorithm and PNN. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). :184–188.
Network security became a viral topic nowadays, Anomaly-based Intrusion Detection Systems [1] (IDSs) plays an indispensable role in identifying the attacks from networks and the detection rate and accuracy are said to be high. The proposed work explore this topic and solve this issue by the IDS model developed using Artificial Neural Network (ANN). This model uses Feed - Forward Neural Net algorithms and Probabilistic Neural Network and oppositional based on Particle Swarm optimization Algorithm for lessen the computational overhead and boost the performance level. The whole computing overhead produced in its execution and training are get minimized by the various optimization techniques used in these developed ANN-based IDS system. The experimental study on the developed system tested using the standard NSL-KDD dataset performs well, while compare with other intrusion detection models, built using NN, RB and OPSO algorithms.
Fuchs, Caro, Spolaor, Simone, Nobile, Marco S., Kaymak, Uzay.  2019.  A Swarm Intelligence Approach to Avoid Local Optima in Fuzzy C-Means Clustering. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–6.
Clustering analysis is an important computational task that has applications in many domains. One of the most popular algorithms to solve the clustering problem is fuzzy c-means, which exploits notions from fuzzy logic to provide a smooth partitioning of the data into classes, allowing the possibility of multiple membership for each data sample. The fuzzy c-means algorithm is based on the optimization of a partitioning function, which minimizes inter-cluster similarity. This optimization problem is known to be NP-hard and it is generally tackled using a hill climbing method, a local optimizer that provides acceptable but sub-optimal solutions, since it is sensitive to initialization and tends to get stuck in local optima. In this work we propose an alternative approach based on the swarm intelligence global optimization method Fuzzy Self-Tuning Particle Swarm Optimization (FST-PSO). We solve the fuzzy clustering task by optimizing fuzzy c-means' partitioning function using FST-PSO. We show that this population-based metaheuristics is more effective than hill climbing, providing high quality solutions with the cost of an additional computational complexity. It is noteworthy that, since this particle swarm optimization algorithm is self-tuning, the user does not have to specify additional hyperparameters for the optimization process.
Kalaivani, S., Vikram, A., Gopinath, G..  2019.  An Effective Swarm Optimization Based Intrusion Detection Classifier System for Cloud Computing. 2019 5th International Conference on Advanced Computing Communication Systems (ICACCS). :185–188.
Most of the swarm optimization techniques are inspired by the characteristics as well as behaviour of flock of birds whereas Artificial Bee Colony is based on the foraging characteristics of the bees. However, certain problems which are solved by ABC do not yield desired results in-terms of performance. ABC is a new devised swarm intelligence algorithm and predominately employed for optimization of numerical problems. The main reason for the success of ABC algorithm is that it consists of feature such as fathomable and flexibility when compared to other swarm optimization algorithms and there are many possible applications of ABC. Cloud computing has their limitation in their application and functionality. The cloud computing environment experiences several security issues such as Dos attack, replay attack, flooding attack. In this paper, an effective classifier is proposed based on Artificial Bee Colony for cloud computing. It is evident in the evaluation results that the proposed classifier achieved a higher accuracy rate.
Nataliani, Yessica, Yang, Miin-Shen.  2019.  Feature-Weighted Fuzzy K-Modes Clustering. Proceedings of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence. :63–68.
Fuzzy k-modes (FKM) are variants of fuzzy c-means used for categorical data. The FKM algorithms generally treat feature components with equal importance. However, in clustering process, different feature weights need to be assigned for feature components because some irrelevant features may degrade the performance of the FKM algorithms. In this paper, we propose a novel algorithm, called feature-weighted fuzzy k-modes (FW-FKM), to improve FKM with a feature-weight entropy term such that it can automatically compute different feature weights for categorical data. Some numerical and real data sets are used to compare FW-FKM with some existing methods in the literature. Experimental results and comparisons actually demonstrate these good aspects of the proposed FW-FKM with its effectiveness and usefulness in practice.
Persis, D. Jinil.  2019.  A Bi-objective Routing Model for Underwater Wireless Sensor Network. Proceedings of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence. :78–82.
Underwater wireless communication is a critical and challenging research area wherein acoustic signals are used to transfer data. The Underwater Wireless Sensor Network (UWSN) is used to transmit data sensed by the sensors in the sea bed to the surface sinks through intermediate nodes for seismic surveillance, border security and underwater environment monitoring applications. The nodes comprising of UWSN are battery operated and are subjected to failures leading to connectivity loss. And the propagation delay in sending the data in the form of acoustic signals is found to be high and as the depth increases the transmission delay also increases. Hence, routing in UWSN is a complex problem. The simulation experiments of the delay sensitive protocols are found to minimize the delay at the expense of network throughput which is not acceptable. The energy aware routing protocols on the other hand reduces energy consumption and routing overhead but has high delay involved in transmission. In this study, transmission delay and reliability estimation models are developed using which bi-objective routing model is proposed considering both delay and reliability in route selection. In the simulation studies, the bi-objective model reduced delay on an average by 9% and the reliability of the network is improved by 34% when compared to the delay sensitive and reliable routing strategies.
Li, Zhiyong, Li, Tao, Zhu, Fangdong.  2019.  An Online Password Guessing Method Based on Big Data. Proceedings of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence. :59–62.
Password authentication is the most widely used authentication method in information systems. The traditional proactive password detection method is generally implemented by counting password length, character class number and computing password information entropy to improve password security. However, passwords that pass proactive password detection do not represent that they are secure. In this paper, based on the research of the characteristics of password distribution under big data, we propose an online password guessing method, which collects a dataset of guessing passwords composed of weak passwords, high frequency passwords and personal information related passwords. It is used to guess the 13k password dataset leaked in China's largest ticketing website, China Railways 12306 website. The experimental results show that even if our guess object has passed the strict proactive password detection, we can construct a guessing password dataset contain only 100 passwords, and effectively guess 4.84% of the passwords.
Hibti, Meryem, Baïna, Karim, Benatallah, Boualem.  2019.  Towards Swarm Intelligence Architectural Patterns: an IoT-Big Data-AI-Blockchain convergence perspective. Proceedings of the 4th International Conference on Big Data and Internet of Things. :1–8.
The Internet of Things (IoT) is exploding. It is made up of billions of smart devices -from minuscule chips to mammoth machines - that use wireless technology to talk to each other (and to us). IoT infrastructures can vary from instrumented connected devices providing data externally to smart, and autonomous systems. To accompany data explosion resulting, among others, from IoT, Big data analytics processes examine large data sets to uncover hidden patterns, unknown correlations between collected events, either at a very technical level (incident/anomaly detection, predictive maintenance) or at business level (customer preferences, market trends, revenue opportunities) to provide improved operational efficiency, better customer service, competitive advantages over rival organizations, etc. In order to capitalize business value of the data generated by IoT sensors, IoT, Big Data Analytics/IA need to meet in the middle. One critical use case for IoT is to warn organizations when a product or service is at risk. The aim of this paper is to present a first proposal of IoT-Big Data-IA architectural patterns catalogues with a Blockchain implementation perspective in seek of design methodologies artifacts.
Pamparà, Gary, Engelbrecht, Andries P..  2019.  Evolutionary and swarm-intelligence algorithms through monadic composition. Proceedings of the Genetic and Evolutionary Computation Conference Companion. :1382–1390.
Reproducible experimental work is a vital part of the scientific method. It is a concern that is often, however, overlooked in modern computational intelligence research. Scientific research within the areas of programming language theory and mathematics have made advances that are directly applicable to the research areas of evolutionary and swarm intelligence. Through the use of functional programming and the established abstractions that functional programming provides, it is possible to define the elements of evolutionary and swarm intelligence algorithms as compositional computations. These compositional blocks then compose together to allow the declarations of an algorithm, whilst considering the declaration as a "sub-program". These sub-programs may then be executed at a later time and provide the blueprints of the computation. Storing experimental results within a robust data-set file format, which is widely supported by analysis tools, provides additional flexibility and allows different analysis tools to access datasets in the same efficient manner. This paper presents an open-source software library for evolutionary and swarm-intelligence algorithms which allows the type-safe, compositional, monadic and functional declaration of algorithms while tracking and managing effects (e.g. usage of a random number generator) that directly influences the execution of an algorithm.
2020-01-21
Ye, Hui, Ma, Xiaopeng, Pan, Qingfeng, Fang, Huaqiang, Xiang, Hang, Shao, Tongzhen.  2019.  An Adaptive Approach for Anomaly Detector Selection and Fine-Tuning in Time Series. Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data. :1–7.
The anomaly detection of time series is a hotspot of time series data mining. The own characteristics of different anomaly detectors determine the abnormal data that they are good at. There is no detector can be optimizing in all types of anomalies. Moreover, it still has difficulties in industrial production due to problems such as a single detector can't be optimized at different time windows of the same time series. This paper proposes an adaptive model based on time series characteristics and selecting appropriate detector and run-time parameters for anomaly detection, which is called ATSDLN(Adaptive Time Series Detector Learning Network). We take the time series as the input of the model, and learn the time series representation through FCN. In order to realize the adaptive selection of detectors and run-time parameters according to the input time series, the outputs of FCN are the inputs of two sub-networks: the detector selection network and the run-time parameters selection network. In addition, the way that the variable layer width design of the parameter selection sub-network and the introduction of transfer learning make the model be with more expandability. Through experiments, it is found that ATSDLN can select appropriate anomaly detector and run-time parameters, and have strong expandability, which can quickly transfer. We investigate the performance of ATSDLN in public data sets, our methods outperform other methods in most cases with higher effect and better adaptation. We also show experimental results on public data sets to demonstrate how model structure and transfer learning affect the effectiveness.
Raulamo-Jurvanen, Päivi, Hosio, Simo, Mäntylä, Mika V..  2019.  Practitioner Evaluations on Software Testing Tools. Proceedings of the Evaluation and Assessment on Software Engineering. :57–66.
In software engineering practice, evaluating and selecting the software testing tools that best fit the project at hand is an important and challenging task. In scientific studies of software engineering, practitioner evaluations and beliefs have recently gained interest, and some studies suggest that practitioners find beliefs of peers more credible than empirical evidence. To study how software practitioners evaluate testing tools, we applied online opinion surveys (n=89). We analyzed the reliability of the opinions utilizing Krippendorff's alpha, intra-class correlation coefficient (ICC), and coefficients of variation (CV). Negative binomial regression was used to evaluate the effect of demographics. We find that opinions towards a specific tool can be conflicting. We show how increasing the number of respondents improves the reliability of the estimates measured with ICC. Our results indicate that on average, opinions from seven experts provide a moderate level of reliability. From demographics, we find that technical seniority leads to more negative evaluations. To improve the understanding, robustness, and impact of the findings, we need to conduct further studies by utilizing diverse sources and complementary methods.
Oruganti, Pradeep Sharma, Appel, Matt, Ahmed, Qadeer.  2019.  Hardware-in-Loop Based Automotive Embedded Systems Cybersecurity Evaluation Testbed. Proceedings of the ACM Workshop on Automotive Cybersecurity. :41–44.
This paper explains the work-in-progress on a vehicle safety and security evaluation platform. Since the testing of cyber-attacks on an actual may be costly or dangerous, the platform enables us to evaluate the threat and the risk associated with cyber-attacks in a safe virtual environment. The goal is to integrate vehicle and powertrain models, mobility and network simulators to actual hardware running the control algorithms using CAN communication. Hardware is selected so as to allows expandability and application of wireless modules which will act as additional attack surfaces. In the current paper, the framework and ideology behind is testbed is described and current progress is shown. A simple GPS spoofing attack on a virtual test vehicle is done and some initial results are discussed.
Li, Toby Jia-Jun.  2019.  End User Programing of Intelligent Agents Using Demonstrations and Natural Language Instructions. Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion. :143–144.
End-user programmable intelligent agents that can learn new tasks and concepts from users' explicit instructions are desired. This paper presents our progress on expanding the capabilities of such agents in the areas of task applicability, task generalizability, user intent disambiguation and support for IoT devices through our multi-modal approach of combining programming by demonstration (PBD) with learning from natural language instructions. Our future directions include facilitating better script reuse and sharing, and supporting greater user expressiveness in instructions.
Kaiya, Haruhiko, Muto, Ryoya, Nagano, Kaito, Yoshida, Mizuki.  2019.  Mutual Requirements Evolution by Combining Different Information Systems. Proceedings of the 23rd Pan-Hellenic Conference on Informatics. :159–162.
We propose a method of eliciting requirements for several different systems together. We focus on systems used by one user at the same time become such systems inherently give influences on with other. We expect such influences help a requirements analyst to be aware of unknown requirements of the user. Any modeling notations are used to explore the combination among systems causing such influences because the differences among the notations give diverse viewpoints to the analyst. To specify such mutual influences, we introduce semantic tags represented by stereo types. We also introduce other semantic tags so that the analyst can judge whether the combination brings advantages to the user. We apply our method to an example and we confirm the method works.
Han, Danyang, Yu, Jinsong, Song, Yue, Tang, Diyin, Dai, Jing.  2019.  A Distributed Autonomic Logistics System with Parallel-Computing Diagnostic Algorithm for Aircrafts. 2019 IEEE AUTOTESTCON. :1–8.
The autonomic logistic system (ALS), first used by the U.S. military JSF, is a new conceptional system which supports prognostic and health management system of aircrafts, including such as real-time failure monitoring, remaining useful life prediction and maintenance decisions-making. However, the development of ALS faces some challenges. Firstly, current ALS is mainly based on client/server architecture, which is very complex in a large-scale aircraft control center and software is required to be reconfigured for every accessed node, which will increase the cost and decrease the expandability of deployment for large scale aircraft control centers. Secondly, interpretation of telemetry parameters from the aircraft is a tough task considering various real-time flight conditions, including instructions from controllers, work statements of single machines or machine groups, and intrinsic physical meaning of telemetry parameters. It is troublesome to meet the expectation of full representing the relationship between faults and tests without a standard model. Finally, typical diagnostic algorithms based on dependency matrix are inefficient, especially the temporal waste when dealing with thousands of test points and fault modes, for the reason that the time complexity will increase exponentially as dependency matrix expansion. Under this situation, this paper proposed a distributed ALS under complex operating conditions, which has the following contributions 1) introducing a distributed system based on browser/server architecture, which is divided overall system into primary control system and diagnostic and health assessment platform; 2) designing a novel interface for modelling the interpretation rules of telemetry parameters and the relationship between faults and tests in consideration of multiple elements of aircraft conditions; 3) proposing a promoted diagnostic algorithm under parallel computing in order to decrease the computing time complexity. what's more, this paper develops a construction with 3D viewer of aircraft for user to locate fault points and presents repairment instructions for maintenance personnels based on Interactive Electronic Technical Manual, which supports both online and offline. A practice in a certain aircraft demonstrated the efficiency of improved diagnostic algorithm and proposed ALS.
Chang, Chun-Young, Chien, Lin-Chien, Kuo, En-Chun, Hwang, Yuh-Shyan.  2019.  IoT-Based "All-Round 3D Technology Security Circle" in New Taipei City Police Department. Proceedings of the 2019 International Electronics Communication Conference. :32–36.
New Taipei City is a metropolitan area in Taiwan, with a prosperous economy and a large population. Many parades and public assemblies are held within the jurisdiction and they might have unintended consequences such as traffic, overcrowding, or even serious risks and mishaps. Many cities around the world face similar problems and threats, such as domestic violence in Turkey and the Boston Marathon bombing. Combating new types of public safety threats has become a primary task. Setting up a complete surveillance system at an event site would give a command center access to the site and allow them to respond to an incident in real time. The system's digital records would help track a crime scene, as well as providing insight into how criminals commit crimes. The New Taipei City Police Department has established an "All-Round 3D Technology Security Circle" equipped with various cameras that transmit images back to a command center based on an Internet of Things connection. Innovative technology combined with smart equipment gives three-dimensional coverage of an area to ensure public safety.
Yu, Yang, Hou, Jing, Li, Huan.  2019.  Study on Continuous Internal Audit System Modeling and Application. Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing. :1–6.
Under the information environment the development of Continuous internal audit business model is inevitable and it will generally become the mainstream model. Based on the understanding of internal audit development in enterprises, it's found that most of current internal audit systems stay at post audit as an auxiliary tool of internal auditors in the auditing process, which hastens the application of continuous internal audit. Emerging computer technology is combined in this paper to build an universal continuous internal audit model, which is divided into four phases, based on internal audit system. Finally, based on the tracking error of index fund, this paper makes an applied research on the framework of the established continuous internal audit system.
Xu, Lei, Yuan, Xingliang, Steinfeld, Ron, Wang, Cong, Xu, Chungen.  2019.  Multi-Writer Searchable Encryption: An LWE-Based Realization and Implementation. Proceedings of the 2019 ACM Asia Conference on Computer and Communications Security. :122–133.
Multi-Writer Searchable Encryption, also known as public-key encryption with keyword search(PEKS), serves a wide spectrum of data sharing applications. It allows users to search over encrypted data encrypted via different keys. However, most of the existing PEKS schemes are built on classic security assumptions, which are proven to be untenable to overcome the threats of quantum computers. To address the above problem, in this paper, we propose a lattice-based searchable encryption scheme from the learning with errors (LWE) hardness assumption. Specifically, we observe that the keys of each user in a basic scheme are composed of large-sized matrices and basis of the lattice. To reduce the complexity of key management, our scheme is designed to enable users to directly use their identity for data encryption. We present several optimization techniques for implementation to make our design nearly practical. For completeness, we conduct rigorous security, complexity, and parameter analysis on our scheme, and perform comprehensive evaluations at a commodity machine. With a scenario of 100 users, the cost of key generation for each user is 125s, and the cost of searching a document with 1000 keywords is 13.4ms.
Li, Yuan, Wang, Hongbing, Zhao, Yunlei.  2019.  Delegatable Order-Revealing Encryption. Proceedings of the 2019 ACM Asia Conference on Computer and Communications Security. :134–147.
Order-revealing encryption (ORE) is a basic cryptographic primitive for ciphertext comparisons based on the order relationship of plaintexts while maintaining the privacy of them. In the data era we are experiencing, cross-dataset transactions become ubiquitous in practice. However, almost all the previous ORE schemes can only support comparisons on ciphertexts from the same user, which does not meet the requirement for the multi-user environment. In this work, we introduce and design ORE schemes with delegation functionality, which is referred to as delegatable ORE (DORE). The "delegation" here is an authorization that allows for efficient ciphertext comparisons among different users. To the best of our knowledge, it is the first ORE that allows an user to delegate the comparison privilege for his ciphertexts, which also opens the door for future explorations. At the heart of the construction and analysis of DORE is a new building tool proposed in this work, named delegatable equality-revealing encoding (DERE), which might be of independent interest.
Koh, John S., Bellovin, Steven M., Nieh, Jason.  2019.  Why Joanie Can Encrypt: Easy Email Encryption with Easy Key Management. Proceedings of the Fourteenth EuroSys Conference 2019. :1–16.

Email privacy is of crucial importance. Existing email encryption approaches are comprehensive but seldom used due to their complexity and inconvenience. We take a new approach to simplify email encryption and improve its usability by implementing receiver-controlled encryption: newly received messages are transparently downloaded and encrypted to a locally-generated key; the original message is then replaced. To avoid the problem of moving a single private key between devices, we implement per-device key pairs: only public keys need be synchronized via a simple verification step. Compromising an email account or server only provides access to encrypted emails. We implemented this scheme on several platforms, showing it works with PGP and S/MIME, is compatible with widely used mail clients and email services including Gmail, has acceptable overhead, and that users consider it intuitive and easy to use.

Jurado, Mireya, Smith, Geoffrey.  2019.  Quantifying Information Leakage of Deterministic Encryption. Proceedings of the 2019 ACM SIGSAC Conference on Cloud Computing Security Workshop. :129–139.
In order to protect user data while maintaining application functionality, encrypted databases can use specialized cryptography such as property-revealing encryption, which allows a property of the underlying plaintext values to be computed from the ciphertext. One example is deterministic encryption which ensures that the same plaintext encrypted under the same key will produce the same ciphertext. This technology enables clients to make queries on sensitive data hosted in a cloud server and has considerable potential to protect data. However, the security implications of deterministic encryption are not well understood. We provide a leakage analysis of deterministic encryption through the application of the framework of quantitative information flow. A key insight from this framework is that there is no single "right'' measure by which leakage can be quantified: information flow depends on the operational scenario and different operational scenarios require different leakage measures. We evaluate leakage under three operational scenarios, modeled using three different gain functions, under a variety of prior distributions in order to bring clarity to this problem.
Alexandru, Andreea B., Pappas, George J..  2019.  Encrypted LQG Using Labeled Homomorphic Encryption. Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems. :129–140.
We consider the problem of implementing a Linear Quadratic Gaussian (LQG) controller on a distributed system, while maintaining the privacy of the measurements, state estimates, control inputs and system model. The component sub-systems and actuator outsource the LQG computation to a cloud controller and encrypt their signals and matrices. The encryption scheme used is Labeled Homomorphic Encryption, which supports the evaluation of degree-2 polynomials on encrypted data, by attaching a unique label to each piece of data and using the fact that the outsourced computation is known by the actuator. We write the state estimate update and control computation as multivariate polynomials in the encrypted data and propose an extension to the Labeled Homomorphic Encryption scheme that achieves the evaluation of low-degree polynomials on encrypted data, with degree larger than two. We showcase the numerical results of the proposed protocol for a temperature control application that indicates competitive online times.
2020-01-20
Nguyen-Van, Thanh, Le, Tien-Dat, Nguyen-Anh, Tuan, Nguyen-Ho, Minh-Phuoc, Nguyen-Van, Tuong, Le-Tran, Minh-Quoc, Le, Quang Nhat, Pham, Harry, Nguyen-An, Khuong.  2019.  A System for Scalable Decentralized Random Number Generation. 2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop (EDOCW). :100–103.

Generating public randomness has been significantly demanding and also challenging, especially after the introduction of the Blockchain Technology. Lotteries, smart contracts, and random audits are examples where the reliability of the randomness source is a vital factor. We demonstrate a system of random number generation service for generating fair, tamper-resistant, and verifiable random numbers. Our protocol together with this system is an R&D project aiming at providing a decentralized solution to random number generation by leveraging the blockchain technology along with long-lasting cryptographic primitives including homomorphic encryption, verifiable random functions. The system decentralizes the process of generating random numbers by combining each party's favored value to obtain the final random numbers. Our novel idea is to force each party to encrypt his contribution before making it public. With the help of homomorphic encryption, all encrypted contribution can be combined without performing any decryption. The solution has achieved the properties of unpredictability, tamper-resistance, and public-verifiability. In addition, it only offers a linear overall complexity with respect to the number of parties on the network, which permits great scalability.

Gollamudi, Anitha, Chong, Stephen, Arden, Owen.  2019.  Information Flow Control for Distributed Trusted Execution Environments. 2019 IEEE 32nd Computer Security Foundations Symposium (CSF). :304–30414.

Distributed applications cannot assume that their security policies will be enforced on untrusted hosts. Trusted execution environments (TEEs) combined with cryptographic mechanisms enable execution of known code on an untrusted host and the exchange of confidential and authenticated messages with it. TEEs do not, however, establish the trustworthiness of code executing in a TEE. Thus, developing secure applications using TEEs requires specialized expertise and careful auditing. This paper presents DFLATE, a core security calculus for distributed applications with TEEs. DFLATE offers high-level abstractions that reflect both the guarantees and limitations of the underlying security mechanisms they are based on. The accuracy of these abstractions is exhibited by asymmetry between confidentiality and integrity in our formal results: DFLATE enforces a strong form of noninterference for confidentiality, but only a weak form for integrity. This reflects the asymmetry of the security guarantees of a TEE: a malicious host cannot access secrets in the TEE or modify its contents, but they can suppress or manipulate the sequence of its inputs and outputs. Therefore DFLATE cannot protect against the suppression of high-integrity messages, but when these messages are delivered, their contents cannot have been influenced by an attacker.