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Zouari, J., Hamdi, M., Kim, T. H..  2017.  A privacy-preserving homomorphic encryption scheme for the Internet of Things. 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC). :1939–1944.

The Internet of Things is a disruptive paradigm based on the cooperation of a plethora of heterogeneous smart things to collect, transmit, and analyze data from the ambient environment. To this end, many monitored variables are combined by a data analysis module in order to implement efficient context-aware decision mechanisms. To ensure resource efficiency, aggregation is a long established solution, however it is applicable only in the case of one sensed variable. We extend the use of aggregation to the complex context of IoT by proposing a novel approach for secure cooperation of smart things while granting confidentiality and integrity. Traditional solutions for data concealment in resource constrained devices rely on hop-by-hop or end-to-end encryption, which are shown to be inefficient in our context. We use a more sophisticated scheme relying on homomorphic encryption which is not compromise resilient. We combine fully additive encryption with fully additive secret sharing to fulfill the required properties. Thorough security analysis and performance evaluation show a viable tradeoff between security and efficiency for our scheme.

Zhang, Zhenyong, Wu, Junfeng, Yau, David, Cheng, Peng, Chen, Jiming.  2018.  Secure Kalman Filter State Estimation by Partially Homomorphic Encryption. 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS). :345–346.
Recently, the security of state estimation has been attracting significant research attention due to the need for trustworthy situation awareness in emerging (e.g., industrial) cyber-physical systems. In this paper, we investigate secure estimation based on Kalman filtering (SEKF) using partially homomorphically encrypted data. The encryption will enhance the confidentiality not only of data transmitted in the communication network, but also key system information required by the estimator. We use a multiplicative homomorphic encryption scheme, but with a modified decryption algorithm. SEKF is able to conceal comprehensive information (i.e., system parameters, measurements, and state estimates) aggregated at the sink node of the estimator, while retaining the effectiveness of normal Kalman filtering. Therefore, even if an attacker has gained unauthorized access to the estimator and associated communication channels, he will not be able to obtain sufficient knowledge of the system state to guide the attack, e.g., ensure its stealthiness. We present an implementation structure of the SEKF to reduce the communication overhead compared with traditional secure multiparty computation (SMC) methods. Finally, we demonstrate the effectiveness of the SEKF on an IEEE 9-bus power system.
Zhang, Yueqian, Kantarci, Burak.  2019.  Invited Paper: AI-Based Security Design of Mobile Crowdsensing Systems: Review, Challenges and Case Studies. 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE). :17—1709.
Mobile crowdsensing (MCS) is a distributed sensing paradigm that uses a variety of built-in sensors in smart mobile devices to enable ubiquitous acquisition of sensory data from surroundings. However, non-dedicated nature of MCS results in vulnerabilities in the presence of malicious participants to compromise the availability of the MCS components, particularly the servers and participants' devices. In this paper, we focus on Denial of Service attacks in MCS where malicious participants submit illegitimate task requests to the MCS platform to keep MCS servers busy while having sensing devices expend energy needlessly. After reviewing Artificial Intelligence-based security solutions for MCS systems, we focus on a typical location-based and energy-oriented DoS attack, and present a security solution that applies ensemble techniques in machine learning to identify illegitimate tasks and prevent personal devices from pointless energy consumption so as to improve the availability of the whole system. Through simulations, we show that ensemble techniques are capable of identifying illegitimate and legitimate tasks while gradient boosting appears to be a preferable solution with an AUC performance higher than 0.88 in the precision-recall curve. We also investigate the impact of environmental settings on the detection performance so as to provide a clearer understanding of the model. Our performance results show that MCS task legitimacy decisions with high F-scores are possible for both illegitimate and legitimate tasks.
Zhang, Yang, Chen, Pengfei, Hao, Long.  2019.  Research on Privacy Protection with Weak Security Network Coding for Mobile Computing. 2019 Seventh International Conference on Advanced Cloud and Big Data (CBD). :174—179.

With the rapid development of the contemporary society, wide use of smart phone and vehicle sensing devices brings a huge influence on the extensive data collection. Network coding can only provide weak security privacy protection. Aiming at weak secure feature of network coding, this paper proposes an information transfer mechanism, Weak Security Network Coding with Homomorphic Encryption (HE-WSNC), and it is integrated into routing policy. In this mechanism, a movement model is designed, which allows information transmission process under Wi-Fi and Bluetooth environment rather than consuming 4G data flow. Not only does this application reduce the cost, but also improve reliability of data transmission. Moreover, it attracts more users to participate.

Zhang, R., Yang, G., Wang, Y..  2018.  Propagation Characteristics of Acoustic Emission Signals in Multi Coupling Interface of the Engine. 2018 IEEE 3rd International Conference on Integrated Circuits and Microsystems (ICICM). :254–258.
The engine is a significant and dynamic component of the aircraft. Because of the complicated structure and severe operating environment, the fault detection of the engine has always been the key and difficult issue in the field of reliability. Based on an engine and the acoustic emission technology, we propose a method of identifying fault types and determining different components in the engine by constructing the attenuation coefficient. There are several common faults of engines, and three different types of fault sources are generated experimentally in this work. Then the fault signal of the above fault sources propagating in different engine components are obtained. Finally, the acoustic emission characteristics of the fault signal are extracted and judged by the attenuation coefficient. The work effectively identifies different types of faults and studies the effects of different structural components on the propagation of fault acoustic emission signals, which provides a method for the use of acoustic emission technology to identify the faults types of the engine and to study the propagation characteristics of AE signals on the engine.*
Zhang, R., Zhu, Q..  2017.  A game-theoretic defense against data poisoning attacks in distributed support vector machines. 2017 IEEE 56th Annual Conference on Decision and Control (CDC). :4582–4587.

With a large number of sensors and control units in networked systems, distributed support vector machines (DSVMs) play a fundamental role in scalable and efficient multi-sensor classification and prediction tasks. However, DSVMs are vulnerable to adversaries who can modify and generate data to deceive the system to misclassification and misprediction. This work aims to design defense strategies for DSVM learner against a potential adversary. We use a game-theoretic framework to capture the conflicting interests between the DSVM learner and the attacker. The Nash equilibrium of the game allows predicting the outcome of learning algorithms in adversarial environments, and enhancing the resilience of the machine learning through dynamic distributed algorithms. We develop a secure and resilient DSVM algorithm with rejection method, and show its resiliency against adversary with numerical experiments.

Zhang, Kewang, Zahng, Qiong.  2018.  Preserve Location Privacy for Cyber-Physical Systems with Addresses Hashing at Data Link Layer. 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). :1028–1032.
Due to their low complexity and robustness in nature, wireless sensor networks are a key component in cyber-physical system. The integration of wireless sensor network in cyber-physical system provides immense benefits in distributed controlled environment. However, the open nature of the wireless medium makes resource-constrained WSN vulnerable to unauthorized interception and detection. Privacy is becoming one of the major issues that jeopardize the successful deployment of WSN. In this paper, we propose a scheme named HASHA to provide location privacy. Different from previous approaches, HASHA protect nodes' location privacy at data link layer. It is well known that payload at data link layer frame is well protected through cryptosystem, but addresses at data link layer leaves unprotected. The adversaries can identify nodes in the network easily by capturing frames and check the source and destination addresses. If both addresses are well protected and unknown to the adversaries, they cannot identify nodes of the targeted networks, rendering it very difficult to launch traffic analysis and locate subjects. Simulation and analytical results demonstrate that our scheme provides stronger privacy protection and requires much less energy.
Zeitz, K., Cantrell, M., Marchany, R., Tront, J..  2017.  Designing a Micro-moving Target IPv6 Defense for the Internet of Things. 2017 IEEE/ACM Second International Conference on Internet-of-Things Design and Implementation (IoTDI). :179–184.

As the use of low-power and low resource embedded devices continues to increase dramatically with the introduction of new Internet of Things (IoT) devices, security techniques are necessary which are compatible with these devices. This research advances the knowledge in the area of cyber security for the IoT through the exploration of a moving target defense to apply for limiting the time attackers may conduct reconnaissance on embedded systems while considering the challenges presented from IoT devices such as resource and performance constraints. We introduce the design and optimizations for a Micro-Moving Target IPv6 Defense including a description of the modes of operation, needed protocols, and use of lightweight hash algorithms. We also detail the testing and validation possibilities including a Cooja simulation configuration, and describe the direction to further enhance and validate the security technique through large scale simulations and hardware testing followed by providing information on other future considerations.

Zayene, M., Habachi, O., Meghdadi, V., Ezzeddine, T., Cances, J. P..  2017.  Joint delay and energy minimization for Wireless Sensor Networks using instantly decodable network coding. 2017 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC). :21–25.

Most of Wireless Sensor Networks (WSNs) are usually deployed in hostile environments where the communications conditions are not stable and not reliable. Hence, there is a need to design an effective distributed schemes to enable the sensors cooperating in order to recover the sensed data. In this paper, we establish a novel cooperative data exchange (CDE) scheme using instantly decodable network coding (IDNC) across the sensor nodes. We model the problem using the cooperative game theory in partition form. We develop also a distributed merge-and-split algorithm in order to form dynamically coalitions that maximize their utilities in terms of both energy consumption and IDNC delay experienced by all sensors. Indeed, the proposed algorithm enables these sensors to self-organize into stable clustered network structure where all sensors do not have incentives to change the cluster he is part of. Simulation results show that our cooperative scheme allows nodes not only to reduce the energy consumption, but also the IDNC completion time.

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Yu, Z., Du, H., Xiao, D., Wang, Z., Han, Q., Guo, B..  2018.  Recognition of Human Computer Operations Based on Keystroke Sensing by Smartphone Microphone. IEEE Internet of Things Journal. 5:1156–1168.

Human computer operations such as writing documents and playing games have become popular in our daily lives. These activities (especially if identified in a non-intrusive manner) can be used to facilitate context-aware services. In this paper, we propose to recognize human computer operations through keystroke sensing with a smartphone. Specifically, we first utilize the microphone embedded in a smartphone to sense the input audio from a computer keyboard. We then identify keystrokes using fingerprint identification techniques. The determined keystrokes are then corrected with a word recognition procedure, which utilizes the relations of adjacent letters in a word. Finally, by fusing both semantic and acoustic features, a classification model is constructed to recognize four typical human computer operations: 1) chatting; 2) coding; 3) writing documents; and 4) playing games. We recruited 15 volunteers to complete these operations, and evaluated the proposed approach from multiple aspects in realistic environments. Experimental results validated the effectiveness of our approach.

Young Sil Lee, Alasaarela, E., Hoonjae Lee.  2014.  Secure key management scheme based on ECC algorithm for patient's medical information in healthcare system. Information Networking (ICOIN), 2014 International Conference on. :453-457.

Recent advances in Wireless Sensor Networks have given rise to many application areas in healthcare such as the new field of Wireless Body Area Networks. The health status of humans can be tracked and monitored using wearable and non-wearable sensor devices. Security in WBAN is very important to guarantee and protect the patient's personal sensitive data and establishing secure communications between BAN sensors and external users is key to addressing prevalent security and privacy concerns. In this paper, we propose secure and efficient key management scheme based on ECC algorithm to protect patient's medical information in healthcare system. Our scheme divided into three phases as setup, registration, verification and key exchange. And we use the identification code which is the SIM card number on a patient's smart phone with the private key generated by the legal use instead of the third party. Also to prevent the replay attack, we use counter number at every process of authenticated message exchange to resist.

Yilin Mo, Sinopoli, B..  2015.  Secure Estimation in the Presence of Integrity Attacks. Automatic Control, IEEE Transactions on. 60:1145-1151.

We consider the estimation of a scalar state based on m measurements that can be potentially manipulated by an adversary. The attacker is assumed to have full knowledge about the true value of the state to be estimated and about the value of all the measurements. However, the attacker has limited resources and can only manipulate up to l of the m measurements. The problem is formulated as a minimax optimization, where one seeks to construct an optimal estimator that minimizes the “worst-case” expected cost against all possible manipulations by the attacker. We show that if the attacker can manipulate at least half the measurements (l ≥ m/2), then the optimal worst-case estimator should ignore all measurements and be based solely on the a-priori information. We provide the explicit form of the optimal estimator when the attacker can manipulate less than half the measurements (l <; m/2), which is based on (m2l) local estimators. We further prove that such an estimator can be reduced into simpler forms for two special cases, i.e., either the estimator is symmetric and monotone or m = 2l + 1. Finally we apply the proposed methodology in the case of Gaussian measurements.

Yap, B. L., Baskaran, V. M..  2016.  Active surveillance using depth sensing technology \#8212; Part I: Intrusion detection. 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW). :1–2.

In part I of a three-part series on active surveillance using depth-sensing technology, this paper proposes an algorithm to identify outdoor intrusion activities by monitoring skeletal positions from Microsoft Kinect sensor in real-time. This algorithm implements three techniques to identify a premise intrusion. The first technique observes a boundary line along the wall (or fence) of a surveilled premise for skeletal trespassing detection. The second technique observes the duration of a skeletal object within a region of a surveilled premise for loitering detection. The third technique analyzes the differences in skeletal height to identify wall climbing. Experiment results suggest that the proposed algorithm is able to detect trespassing, loitering and wall climbing at a rate of 70%, 85% and 80% respectively.

Yao, Manting, Yuan, Weina, Wang, Nan, Zhang, Zeyu, Qiu, Yuan, Liu, Yichuan.  2020.  SS3: Security-Aware Vendor-Constrained Task Scheduling for Heterogeneous Multiprocessor System-on-Chips. 2020 IEEE International Conference on Networking, Sensing and Control (ICNSC). :1–6.
Design for trust approaches can protect an MPSoC system from hardware Trojan attack due to the high penetration of third-party intellectual property. However, this incurs significant design cost by purchasing IP cores from various IP vendors, and the IP vendors providing particular IP are always limited, making these approaches unable to be performed in practice. This paper treats IP vendor as constraint, and tasks are scheduled with a minimized security constraint violations, furthermore, the area of MPSoC is also optimized during scheduling. Experimental results demonstrate the effectiveness of our proposed algorithm, by reducing 0.37% security constraint violations.
Yang, Xudong, Gao, Ling, Wang, Hai, Zheng, Jie, Guo, Hongbo.  2019.  A Semantic k-Anonymity Privacy Protection Method for Publishing Sparse Location Data. 2019 Seventh International Conference on Advanced Cloud and Big Data (CBD). :216—222.

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.

Yang, B., Liu, F., Yuan, L., Zhang, Y..  2020.  6LoWPAN Protocol Based Infrared Sensor Network Human Target Locating System. 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA). :1773–1779.
This paper proposes an infrared sensor human target locating system for the Internet of Things. In this design, the wireless sensor network is designed and developed to detect human targets by using 6LoWPAN protocol and pyroelectric infrared (PIR) sensors. Based on the detection data acquired by multiple sensor nodes, K-means++ clustering algorithm combined with cost function is applied to complete human target location in a 10m×10m detection area. The experimental results indicate the human locating system works well and the user can view the location information on the terminal devices.
Yamaguchi, S..  2020.  Botnet Defense System and Its Basic Strategy Against Malicious Botnet. 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan). :1—2.

This paper proposes a basic strategy for Botnet Defense System (BDS). BDS is a cybersecurity system that utilizes white-hat botnets to defend IoT systems against malicious botnets. Once a BDS detects a malicious botnet, it launches white-hat worms in order to drive out the malicious botnet. The proposed strategy aims at the proper use of the worms based on the worms' capability such as lifespan and secondary infectivity. If the worms have high secondary infectivity or a long lifespan, the BDS only has to launch a few worms. Otherwise, it should launch as many worms as possible. The effectiveness of the strategy was confirmed through the simulation evaluation using agent-oriented Petri nets.

Yadav, S., Howells, G..  2017.  Analysis of ICMetrics Features/Technology for Wearable Devices IOT Sensors. 2017 Seventh International Conference on Emerging Security Technologies (EST). :175–178.

This paper investigates the suitability of employing various measurable features derived from multiple wearable devices (Apple Watch), for the generation of unique authentication and encryption keys related to the user. This technique is termed as ICMetrics. The ICMetrics technology requires identifying the suitable features in an environment for key generation most useful for online services. This paper presents an evaluation of the feasibility of identifying a unique user based on desirable feature set and activity data collected over short and long term and explores how the number of samples being factored into the ICMetrics system affects uniqueness of the key.

Yadav, Kuldeep, Roy, Sanjay Dhar, Kundu, Sumit.  2018.  Total Error Reduction in Presence of Malicious User in a Cognitive Radio Network. 2018 2nd International Conference on Electronics, Materials Engineering Nano-Technology (IEMENTech). :1-4.

Primary user emulation (PUE) attack causes security issues in a cognitive radio network (CRN) while sensing the unused spectrum. In PUE attack, malicious users transmit an emulated primary signal in spectrum sensing interval to secondary users (SUs) to forestall them from accessing the primary user (PU) spectrum bands. In the present paper, the defense against such attack by Neyman-Pearson criterion is shown in terms of total error probability. Impact of several parameters such as attacker strength, attacker's presence probability, and signal-to-noise ratio on SU is shown. Result shows proposed method protect the harmful effects of PUE attack in spectrum sensing.

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Xu, Yue, Ni, Ming, Ying, Fei, Zhang, Jingwen.  2020.  Security Optimization Based on Mimic Common Operating Environment for the Internet of Vehicles. 2020 2nd International Conference on Computer Communication and the Internet (ICCCI). :18—23.
The increasing vehicles have brought convenience to people as well as many traffic problems. The Internet of Vehicles (IoV) is an extension of the intelligent transportation system based on the Internet of Things (IoT), which is the omnibearing network connection among “Vehicles, Loads, Clouds”. However, IoV also faces threats from various known and unknown security vulnerabilities. Traditional security defense methods can only deal with known attacks, while there is no effective way to deal with unknown attacks. In this paper, we show an IoV system deployed on a Mimic Common Operating Environment (MCOE). At the sensing layer, we introduce a lightweight cryptographic algorithm, LBlock, to encrypt the data collected by the hardware. Thus, we can prevent malicious tampering of information such as vehicle conditions. At the application layer, we firstly put the IoV system platform into MCOE to make it dynamic, heterogeneous and redundant. Extensive experiments prove that the sensing layer can encrypt data reliably and energy-efficiently. And we prove the feasibility and security of the Internet of Vehicles system platform on MCOE.
Xu, M., Huber, M., Sun, Z., England, P., Peinado, M., Lee, S., Marochko, A., Mattoon, D., Spiger, R., Thom, S..  2019.  Dominance as a New Trusted Computing Primitive for the Internet of Things. 2019 IEEE Symposium on Security and Privacy (SP). :1415–1430.
The Internet of Things (IoT) is rapidly emerging as one of the dominant computing paradigms of this decade. Applications range from in-home entertainment to large-scale industrial deployments such as controlling assembly lines and monitoring traffic. While IoT devices are in many respects similar to traditional computers, user expectations and deployment scenarios as well as cost and hardware constraints are sufficiently different to create new security challenges as well as new opportunities. This is especially true for large-scale IoT deployments in which a central entity deploys and controls a large number of IoT devices with minimal human interaction. Like traditional computers, IoT devices are subject to attack and compromise. Large IoT deployments consisting of many nearly identical devices are especially attractive targets. At the same time, recovery from root compromise by conventional means becomes costly and slow, even more so if the devices are dispersed over a large geographical area. In the worst case, technicians have to travel to all devices and manually recover them. Data center solutions such as the Intelligent Platform Management Interface (IPMI) which rely on separate service processors and network connections are not only not supported by existing IoT hardware, but are unlikely to be in the foreseeable future due to the cost constraints of mainstream IoT devices. This paper presents CIDER, a system that can recover IoT devices within a short amount of time, even if attackers have taken root control of every device in a large deployment. The recovery requires minimal manual intervention. After the administrator has identified the compromise and produced an updated firmware image, he/she can instruct CIDER to force the devices to reset and to install the patched firmware on the devices. We demonstrate the universality and practicality of CIDER by implementing it on three popular IoT platforms (HummingBoard Edge, Raspberry Pi Compute Module 3 and Nucleo-L476RG) spanning the range from high to low end. Our evaluation shows that the performance overhead of CIDER is generally negligible.
Xie, Kun, Li, Xiaocan, Wang, Xin, Xie, Gaogang, Xie, Dongliang, Li, Zhenyu, Wen, Jigang, Diao, Zulong.  2019.  Quick and Accurate False Data Detection in Mobile Crowd Sensing. IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. :2215—2223.

With the proliferation of smartphones, a novel sensing paradigm called Mobile Crowd Sensing (MCS) has emerged very recently. However, the attacks and faults in MCS cause a serious false data problem. Observing the intrinsic low dimensionality of general monitoring data and the sparsity of false data, false data detection can be performed based on the separation of normal data and anomalies. Although the existing separation algorithm based on Direct Robust Matrix Factorization (DRMF) is proven to be effective, requiring iteratively performing Singular Value Decomposition (SVD) for low-rank matrix approximation would result in a prohibitively high accumulated computation cost when the data matrix is large. In this work, we observe the quick false data location feature from our empirical study of DRMF, based on which we propose an intelligent Light weight Low Rank and False Matrix Separation algorithm (LightLRFMS) that can reuse the previous result of the matrix decomposition to deduce the one for the current iteration step. Our algorithm can largely speed up the whole iteration process. From a theoretical perspective, we validate that LightLRFMS only requires one round of SVD computation and thus has very low computation cost. We have done extensive experiments using a PM 2.5 air condition trace and a road traffic trace. Our results demonstrate that LightLRFMS can achieve very good false data detection performance with the same highest detection accuracy as DRMF but with up to 10 times faster speed thanks to its lower computation cost.

Xiao, Litian, Xiao, Nan, Li, Mengyuan, Liu, Zhanqing, Wang, Fei, Li, Yuliang, Hou, Kewen.  2019.  Intelligent Architecture and Hybrid Model of Ground and Launch System for Advanced Launch Site. 2019 IEEE Aerospace Conference. :1–12.
This paper proposes an intelligent functional architecture for an advanced launch site system that is composed of five parts: the intelligent technical area, the intelligent launching region, the intelligent flight and landing area, the intelligent command and control system, and the intelligent analysis assessment system. The five parts consist of the infrastructure, facilities, equipment, hardware and software and thus include the whole mission processes of ground and launch systems from flight articles' entry to launch. The architectural framework is designed for the intelligent elements of the parts. The framework is also defined as the interrelationship and the interface of the elements, including the launch vehicle and flight payloads. Based on the Internet of Things (IoT), the framework is integrated on four levels: the physical layer, the perception layer, the network layer, and the application layer. The physical layer includes the physical objects and actuators of the launch site. The perception layer consists of the sensors and data processing system. The network layer supplies the access gateways and backbone network. The application layer serves application systems through the middleware platform. The core of the intelligent system is the controller of the automatic control system crossing the four layers. This study builds the models of the IoT, cloud platform, middleware, integrated access gateway, and automatic control system for actual ground and launch systems. A formal approach describes and defines the architecture, models and autonomous control flows in the paper. The defined models describe the physical objects, intelligent elements, interface relations, status transformation functions, etc. The test operation and launch processes are connected with the intelligent system model. This study has been applied to an individual mission project and achieved good results. The architecture and the models of this study regulate the relationship between the elements of the intelligent system. The study lays a foundation for the architectural construction, the simulation and the verification of the intelligent systems at the launch site.
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Wiesner, K., Feld, S., Dorfmeister, F., Linnhoff-Popien, C..  2014.  Right to silence: Establishing map-based Silent Zones for participatory sensing. Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2014 IEEE Ninth International Conference on. :1-6.

Participatory sensing tries to create cost-effective, large-scale sensing systems by leveraging sensors embedded in mobile devices. One major challenge in these systems is to protect the users' privacy, since users will not contribute data if their privacy is jeopardized. Especially location data needs to be protected if it is likely to reveal information about the users' identities. A common solution is the blinding out approach that creates so-called ban zones in which location data is not published. Thereby, a user's important places, e.g., her home or workplace, can be concealed. However, ban zones of a fixed size are not able to guarantee any particular level of privacy. For instance, a ban zone that is large enough to conceal a user's home in a large city might be too small in a less populated area. For this reason, we propose an approach for dynamic map-based blinding out: The boundaries of our privacy zones, called Silent Zones, are determined in such way that at least k buildings are located within this zone. Thus, our approach adapts to the habitat density and we can guarantee k-anonymity in terms of surrounding buildings. In this paper, we present two new algorithms for creating Silent Zones and evaluate their performance. Our results show that especially in worst case scenarios, i.e., in sparsely populated areas, our approach outperforms standard ban zones and guarantees the specified privacy level.

Wen, Jinming, Yu, Wei.  2019.  Exact Sparse Signal Recovery via Orthogonal Matching Pursuit with Prior Information. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :5003–5007.
The orthogonal matching pursuit (OMP) algorithm is a commonly used algorithm for recovering K-sparse signals x ∈ ℝn from linear model y = Ax, where A ∈ ℝm×n is a sensing matrix. A fundamental question in the performance analysis of OMP is the characterization of the probability that it can exactly recover x for random matrix A. Although in many practical applications, in addition to the sparsity, x usually also has some additional property (for example, the nonzero entries of x independently and identically follow the Gaussian distribution), none of existing analysis uses these properties to answer the above question. In this paper, we first show that the prior distribution information of x can be used to provide an upper bound on \textbackslashtextbar\textbackslashtextbarx\textbackslashtextbar\textbackslashtextbar21/\textbackslashtextbar\textbackslashtextbarx\textbackslashtextbar\textbackslashtextbar22, and then explore the bound to develop a better lower bound on the probability of exact recovery with OMP in K iterations. Simulation tests are presented to illustrate the superiority of the new bound.