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Xiaoguang Niu, Chuanbo Wei, Weijiang Feng, Qianyuan Chen.  2014.  OSAP: Optimal-cluster-based source anonymity protocol in delay-sensitive wireless sensor networks. Wireless Communications and Networking Conference (WCNC), 2014 IEEE. :2880-2885.

For wireless sensor networks deployed to monitor and report real events, event source-location privacy (SLP) is a critical security property. Previous work has proposed schemes based on fake packet injection such as FitProbRate and TFS, to realize event source anonymity for sensor networks under a challenging attack model where a global attacker is able to monitor the traffic in the entire network. Although these schemes can well protect the SLP, there exists imbalance in traffic or delay. In this paper, we propose an Optimal-cluster-based Source Anonymity Protocol (OSAP), which can achieve a tradeoff between network traffic and real event report latency through adjusting the transmission rate and the radius of unequal clusters, to reduce the network traffic. The simulation results demonstrate that OSAP can significantly reduce the network traffic and the delay meets the system requirement.

Xiaohao, S., Baolong, L..  2017.  An Investigation on Tree-Based Tags Anti-collision Algorithms in RFID. 2017 International Conference on Computer Network, Electronic and Automation (ICCNEA). :5–11.

The tree-based tags anti-collision algorithm is an important method in the anti-collision algorithms. In this paper, several typical tree algorithms are evaluated. The comparison of algorithms is summarized including time complexity, communication complexity and recognition, and the characteristics and disadvantages of each algorithm are pointed out. Finally, the improvement strategies of tree anti-collision algorithm are proposed, and the future research directions are also prospected.

Xiaohe, Cao, Liuping, Feng, Peng, Cao, Jianhua, Hu, Jianle, Zhu.  2017.  Research on Anti-Counterfeiting Technology of Print Image Based on the Metameric Properties. Proceedings of the 2017 2Nd International Conference on Communication and Information Systems. :284–289.
High-precision scanners, copiers and other equipment to copy the image compared with the original, you can achieve a very realistic effect. There is a certain threat to the copyright of the manuscript. In view of this phenomenon, a design method of metameric security images with anti-counterfeiting and anti-copy function is presented on this paper. Metameric security images are designed and printed by the theory of metameric color and the four-color ink spectral characteristics. The realization of anti-counterfeiting function is based on the difference of K ink content in proportion of CMYK. In the metameric security images, trademark image display for the CMYK color, and visible under the sunlight. Anti-counterfeiting images appear as monochrome K ink, and visible under the infrared. The experimental results show that the metameric security images with infrared detection device and its characteristics under the infrared light source are observed the clear hidden information. It realizes the anti-counterfeiting function. The method can be applied to various industries in the trademark image security.
Xiaolei, WANG, Zhengning, YU, Xuemin, NIU, Xianfeng, LU, Hao, YANG, Zhongjiawen, LIU.  2019.  Combination Multiple Faults Diagnosis Method Applied to the Aero-engine Based on Improved Signed Directed Graph. 2019 4th International Conference on Measurement, Information and Control (ICMIC). :1–10.
In signed directed graph (SDG) fault diagnosis model, only single fault can be diagnosed. In order to meet the requirements of multiple faults diagnosis, in this paper, improved signed directed graph (ISDG) fault diagnosis model was proposed. The logic and influence between nodes were included in ISDG model. With ISDG model, complex logic can be shown, multiple faults can be diagnosed and the optimal sequence can be determined. Two algorithms are proposed in this paper. One algorithm can obtain the multiple faults combine logic, and the other algorithm can obtain the optimal path of fault diagnosis. According to these two algorithms, the efficiency was improved and the cost was reduced in the multiple fault diagnosis process. Finally, the faults of an aircraft engine bleed system were diagnosed with the interactive algorithm. The proposed algorithms can obtain a diagnosis result effectively. The results of two cases prove that these algorithms can be used for multiple fault diagnosis.
Xiaoxin, LOU, Xiulan, SONG, Defeng, HE, Liming, MENG.  2019.  Secure estimation for intelligent connected vehicle systems against sensor attacks. 2019 Chinese Control Conference (CCC). :6658–6662.
Intelligent connected vehicle system tightly integrates computing, communication, and control strategy. It can increase the traffic throughput, minimize the risk of accidents and reduce the energy consumption. However, because of the openness of the vehicular ad hoc network, the system is vulnerable to cyber-attacks and may result in disastrous consequences. Hence, it is interesting in design of the connected vehicular systems to be resilient to the sensor attacks. The paper focuses on the estimation and control of the intelligent connected vehicle systems when the sensors or the wireless channels of the system are attacked by attackers. We give the upper bound of the corrupted sensors that can be corrected and design the state estimator to reconstruct the initial state by designing a closed-loop controller. Finally, we verify the algorithm for the connected vehicle system by some classical simulations.
Xiaoyong Li, Huadong Ma, Feng Zhou, Xiaolin Gui.  2015.  Service Operator-Aware Trust Scheme for Resource Matchmaking across Multiple Clouds. Parallel and Distributed Systems, IEEE Transactions on. 26:1419-1429.

This paper proposes a service operator-aware trust scheme (SOTS) for resource matchmaking across multiple clouds. Through analyzing the built-in relationship between the users, the broker, and the service resources, this paper proposes a middleware framework of trust management that can effectively reduces user burden and improve system dependability. Based on multidimensional resource service operators, we model the problem of trust evaluation as a process of multi-attribute decision-making, and develop an adaptive trust evaluation approach based on information entropy theory. This adaptive approach can overcome the limitations of traditional trust schemes, whereby the trusted operators are weighted manually or subjectively. As a result, using SOTS, the broker can efficiently and accurately prepare the most trusted resources in advance, and thus provide more dependable resources to users. Our experiments yield interesting and meaningful observations that can facilitate the effective utilization of SOTS in a large-scale multi-cloud environment.

Xiaoyu, Xu, Huang, Zhiqing, Lin, Zhuying.  2018.  Trajectory-Based Task Allocation for Crowd Sensing in Internet of Vehicles. 2018 International Conference on Robots Intelligent System (ICRIS). :226—231.

Crowd sensing is one of the core features of internet of vehicles, the use of internet of vehicles for crowd sensing is conducive to the rational allocation of sensing tasks. This paper mainly studies the problem of task allocation for crowd sensing in internet of vehicles, proposes a trajectory-based task allocation scheme for crowd sensing in internet of vehicles. With limited budget constraints, participants' trajectory is taken as an indicator of the spatiotemporal availability. Based on the solution idea of the minimal-cover problem, select the minimum number of participating vehicles to achieve the coverage of the target area.

Xie, Cihang, Wu, Yuxin, Maaten, Laurens van der, Yuille, Alan L., He, Kaiming.  2019.  Feature Denoising for Improving Adversarial Robustness. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). :501—509.

Adversarial attacks to image classification systems present challenges to convolutional networks and opportunities for understanding them. This study suggests that adversarial perturbations on images lead to noise in the features constructed by these networks. Motivated by this observation, we develop new network architectures that increase adversarial robustness by performing feature denoising. Specifically, our networks contain blocks that denoise the features using non-local means or other filters; the entire networks are trained end-to-end. When combined with adversarial training, our feature denoising networks substantially improve the state-of-the-art in adversarial robustness in both white-box and black-box attack settings. On ImageNet, under 10-iteration PGD white-box attacks where prior art has 27.9% accuracy, our method achieves 55.7%; even under extreme 2000-iteration PGD white-box attacks, our method secures 42.6% accuracy. Our method was ranked first in Competition on Adversarial Attacks and Defenses (CAAD) 2018 — it achieved 50.6% classification accuracy on a secret, ImageNet-like test dataset against 48 unknown attackers, surpassing the runner-up approach by 10%. Code is available at

Xie, D., Wang, Y..  2017.  High definition wide dynamic video surveillance system based on FPGA. 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). :2403–2407.

A high definition(HD) wide dynamic video surveillance system is designed and implemented based on Field Programmable Gate Array(FPGA). This system is composed of three subsystems, which are video capture, video wide dynamic processing and video display subsystem. The images in the video are captured directly through the camera that is configured in a pattern have long exposure in odd frames and short exposure in even frames. The video data stream is buffered in DDR2 SDRAM to obtain two adjacent frames. Later, the image data fusion is completed by fusing the long exposure image with the short exposure image (pixel by pixel). The video image display subsystem can display the image through a HDMI interface. The system is designed on the platform of Lattice ECP3-70EA FPGA, and camera is the Panasonic MN34229 sensor. The experimental result shows that this system can expand dynamic range of the HD video with 30 frames per second and a resolution equal to 1920*1080 pixels by real-time wide dynamic range (WDR) video processing, and has a high practical value.

Xie, H., Lv, K., Hu, C..  2018.  An Improved Monte Carlo Graph Search Algorithm for Optimal Attack Path Analysis. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :307-315.

The problem of optimal attack path analysis is one of the hotspots in network security. Many methods are available to calculate an optimal attack path, such as Q-learning algorithm, heuristic algorithms, etc. But most of them have shortcomings. Some methods can lead to the problem of path loss, and some methods render the result un-comprehensive. This article proposes an improved Monte Carlo Graph Search algorithm (IMCGS) to calculate optimal attack paths in target network. IMCGS can avoid the problem of path loss and get comprehensive results quickly. IMCGS is divided into two steps: selection and backpropagation, which is used to calculate optimal attack paths. A weight vector containing priority, host connection number, CVSS value is proposed for every host in an attack path. This vector is used to calculate the evaluation value, the total CVSS value and the average CVSS value of a path in the target network. Result for a sample test network is presented to demonstrate the capabilities of the proposed algorithm to generate optimal attack paths in one single run. The results obtained by IMCGS show good performance and are compared with Ant Colony Optimization Algorithm (ACO) and k-zero attack graph.

Xie, J., Zhang, M., Ma, Y..  2019.  Using Format Migration and Preservation Metadata to Support Digital Preservation of Scientific Data. 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS). :1—6.

With the development of e-Science and data intensive scientific discovery, it needs to ensure scientific data available for the long-term, with the goal that the valuable scientific data should be discovered and re-used for downstream investigations, either alone, or in combination with newly generated data. As such, the preservation of scientific data enables that not only might experiment be reproducible and verifiable, but also new questions can be raised by other scientists to promote research and innovation. In this paper, we focus on the two main problems of digital preservation that are format migration and preservation metadata. Format migration includes both format verification and object transformation. The system architecture of format migration and preservation metadata is presented, mapping rules of object transformation are analyzed, data fixity and integrity and authenticity, digital signature and so on are discussed and an example is shown in detail.

Xie, J., Chen, Y., Wang, L., Wang, Z..  2020.  A Network Covert Timing Channel Detection Method Based on Chaos Theory and Threshold Secret Sharing. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 1:2380—2384.

Network covert timing channel(NCTC) is a process of transmitting hidden information by means of inter-packet delay (IPD) of legitimate network traffic. Their ability to evade traditional security policies makes NCTCs a grave security concern. However, a robust method that can be used to detect a large number of NCTCs is missing. In this paper, a NCTC detection method based on chaos theory and threshold secret sharing is proposed. Our method uses chaos theory to reconstruct a high-dimensional phase space from one-dimensional time series and extract the unique and stable channel traits. Then, a channel identifier is constructed using the secret reconstruction strategy from threshold secret sharing to realize the mapping of the channel features to channel identifiers. Experimental results show that the approach can detect varieties of NCTCs with a guaranteed true positive rate and greatly improve the versatility and robustness.

Xie, J., She, H., Chen, X., Zhang, H., Niu, Y..  2020.  Test Method for Automatic Detection Capability of Civil Aviation Security Equipment Using Bayesian Estimation. 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT. :831–835.
There are a lot of emerging security equipment required to be tested on detection rate (DR) and false alarm rate (FAR) for prohibited items. This article imports Bayesian approach to accept or reject DR and FAR. The detailed quantitative predictions can be made through the posterior distribution obtained by Markov chain Monte Carlo method. Based on this, HDI + ROPE decision rule is established. For the tests that need to make early decision, HDI + ROPE stopping rule is presented with biased estimate value, and criterial precision rule is presented with unbiased estimate value. Choosing the stopping rule according to the test purpose can achieve the balance of efficiency and accuracy.
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.

Xie, L. F., Ho, I. W., Situ, Z., Li, P..  2020.  The Impact of CFO on OFDM based Physical-layer Network Coding with QPSK Modulation. 2020 IEEE Wireless Communications and Networking Conference (WCNC). :1—6.
This paper studies Physical-layer Network Coding (PNC) in a two-way relay channel (TWRC) operated based on OFDM and QPSK modulation but with the presence of carrier frequency offset (CFO). CFO, induced by node motion and/or oscillator mismatch, causes inter-carrier interference (ICI) that impairs received signals in PNC. Our ultimate goal is to empower the relay in TWRC to decode network-coded information of the end users at a low bit error rate (BER) under CFO, as it is impossible to eliminate the CFO of both end users. For that, we first put forth two signal detection and channel decoding schemes at the relay in PNC. For signal detection, both schemes exploit the signal structure introduced by ICI, but they aim for different output, thus differing in the subsequent channel decoding. We then consider CFO compensation that adjusts the CFO values of the end nodes simultaneously and find that an optimal choice is to yield opposite CFO values in PNC. Particularly, we reveal that pilot insertion could play an important role against the CFO effect, indicating that we may trade more pilots for not just a better channel estimation but also a lower BER at the relay in PNC. With our proposed measures, we conduct simulation using repeat-accumulate (RA) codes and QPSK modulation to show that PNC can achieve a BER at the relay comparable to that of point-to-point transmissions for low to medium CFO levels.
Xie, Lanchi, Xu, Lei, Zhang, Ning, Guo, Jingjing, Yan, Yuwen, Li, Zhihui, Li, Zhigang, Xu, Xiaojing.  2016.  Improved Face Recognition Result Reranking Based on Shape Contexts. Proceedings of the 2016 International Conference on Intelligent Information Processing. :11:1–11:6.

Automatic face recognition techniques applied on particular group or mass database introduces error cases. Error prevention is crucial for the court. Reranking of recognition results based on anthropology analysis can significant improve the accuracy of automatic methods. Previous studies focused on manual facial comparison. This paper proposed a weighted facial similarity computing method based on morphological analysis of components characteristics. Search sequence of face recognition reranked according to similarity, while the interference terms can be removed. Within this research project, standardized photographs, surveillance videos, 3D face images, identity card photographs of 241 male subjects from China were acquired. Sequencing results were modified by modeling selected individual features from the DMV altas. The improved method raises the accuracy of face recognition through anthroposophic or morphologic theory.

Xie, P., Feng, J., Cao, Z., Wang, J..  2017.  GeneWave: Fast authentication and key agreement on commodity mobile devices. 2017 IEEE 25th International Conference on Network Protocols (ICNP). :1–10.
Device-to-device (D2D) communication is widely used for mobile devices and Internet of Things (IoT). Authentication and key agreement are critical to build a secure channel between two devices. However, existing approaches often rely on a pre-built fingerprint database and suffer from low key generation rate. We present GeneWave, a fast device authentication and key agreement protocol for commodity mobile devices. GeneWave first achieves bidirectional initial authentication based on the physical response interval between two devices. To keep the accuracy of interval estimation, we eliminate time uncertainty on commodity devices through fast signal detection and redundancy time cancellation. Then we derive the initial acoustic channel response (ACR) for device authentication. We design a novel coding scheme for efficient key agreement while ensuring security. Therefore, two devices can authenticate each other and securely agree on a symmetric key. GeneWave requires neither special hardware nor pre-built fingerprint database, and thus it is easy-to-use on commercial mobile devices. We implement GeneWave on mobile devices (i.e., Nexus 5X and Nexus 6P) and evaluate its performance through extensive experiments. Experimental results show that GeneWave efficiently accomplish secure key agreement on commodity smartphones with a key generation rate 10x faster than the state-of-the-art approach.
Xie, P., Feng, J., Cao, Z., Wang, J..  2018.  GeneWave: Fast Authentication and Key Agreement on Commodity Mobile Devices. IEEE/ACM Transactions on Networking. 26:1688–1700.

Device-to-device communication is widely used for mobile devices and Internet of Things. Authentication and key agreement are critical to build a secure channel between two devices. However, existing approaches often rely on a pre-built fingerprint database and suffer from low key generation rate. We present GeneWave, a fast device authentication and key agreement protocol for commodity mobile devices. GeneWave first achieves bidirectional initial authentication based on the physical response interval between two devices. To keep the accuracy of interval estimation, we eliminate time uncertainty on commodity devices through fast signal detection and redundancy time cancellation. Then, we derive the initial acoustic channel response for device authentication. We design a novel coding scheme for efficient key agreement while ensuring security. Therefore, two devices can authenticate each other and securely agree on a symmetric key. GeneWave requires neither special hardware nor pre-built fingerprint database, and thus it is easyto-use on commercial mobile devices. We implement GeneWave on mobile devices (i.e., Nexus 5X and Nexus 6P) and evaluate its performance through extensive experiments. Experimental results show that GeneWave efficiently accomplish secure key agreement on commodity smartphones with a key generation rate 10× faster than the state-of-the-art approach.

Xie, S., Wang, G..  2018.  Optimization of parallel turnings using particle swarm intelligence. 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI). :230–234.
Machining process parameters optimization is of concern in machining fields considering machining cost factor. In order to solve the optimization problem of machining process parameters in parallel turning operations, which aims to reduce the machining cost, two PSO-based optimization approaches are proposed in this paper. According to the divide-and-conquer idea, the problem is divided into some similar sub-problems. A particle swarm optimization then is derived to conquer each sub-problem to find the optimal results. Simulations show that, comparing to other optimization approaches proposed previously, the proposed two PSO-based approaches can get optimal machining parameters to reduce both the machining cost (UC) and the computation time.
Xie, T., Zhou, Q., Hu, J., Shu, L., Jiang, P..  2017.  A Sequential Multi-Objective Robust Optimization Approach under Interval Uncertainty Based on Support Vector Machines. 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). :2088–2092.

Interval uncertainty can cause uncontrollable variations in the objective and constraint values, which could seriously deteriorate the performance or even change the feasibility of the optimal solutions. Robust optimization is to obtain solutions that are optimal and minimally sensitive to uncertainty. In this paper, a sequential multi-objective robust optimization (MORO) approach based on support vector machines (SVM) is proposed. Firstly, a sequential optimization structure is adopted to ease the computational burden. Secondly, SVM is used to construct a classification model to classify design alternatives into feasible or infeasible. The proposed approach is tested on a numerical example and an engineering case. Results illustrate that the proposed approach can reasonably approximate solutions obtained from the existing sequential MORO approach (SMORO), while the computational costs are significantly reduced compared with those of SMORO.

Xie, Tao, Enck, William.  2016.  Text Analytics for Security: Tutorial. Proceedings of the Symposium and Bootcamp on the Science of Security. :124–125.

Computing systems that make security decisions often fail to take into account human expectations. This failure occurs because human expectations are typically drawn from in textual sources (e.g., mobile application description and requirements documents) and are hard to extract and codify. Recently, researchers in security and software engineering have begun using text analytics to create initial models of human expectation. In this tutorial, we provide an introduction to popular techniques and tools of natural language processing (NLP) and text mining, and share our experiences in applying text analytics to security problems. We also highlight the current challenges of applying these techniques and tools for addressing security problems. We conclude the tutorial with discussion of future research directions.

Xie, X. L., Xue, W. X..  2018.  An Empirical Study of Web Software Trustworthiness Measurement. 2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC). :1474–1481.

The aim of this paper is to present a fresh methodology of improved evidence synthesis for assessing software trustworthiness, which can unwind collisions stemming from proofs and these proofs' own uncertainties. To achieve this end, the paper, on the ground of ISO/IEC 9126 and web software attributes, models the indicator framework by factor analysis. Then, the paper conducts an calculation of the weight for each indicator via the technique of structural entropy and makes a fuzzy judgment matrix concerning specialists' comments. This study performs a computation of scoring and grade regarding software trustworthiness by using of the criterion concerning confidence degree discernment and comes up with countermeasures to promote trustworthiness. Relying on online accounting software, this study makes an empirical analysis to further confirm validity and robustness. This paper concludes with pointing out limitations.

Xie, Xiongwei, Wang, Weichao.  2016.  Lightweight Examination of DLL Environments in Virtual Machines to Detect Malware. Proceedings of the 4th ACM International Workshop on Security in Cloud Computing. :10–16.

Since it becomes increasingly difficult to trick end users to install and run executable files from unknown sources, attackers refer to stealthy ways such as manipulation of DLL (Dynamic Link Library) files to compromise user computers. In this paper, we propose to develop mechanisms that allow the hypervisor to conduct lightweight examination of DLL files and their running environment in guest virtual machines. Different from the approaches that focus on static analysis of the DLL API calling graphs, our mechanisms conduct continuous examination of their running states. In this way, malicious manipulations to DLL files that happen after they are loaded into memory can also be detected. In order to maintain non-intrusive monitoring and reduce the impacts on VM performance, we avoid examinations of the complete DLL file contents but focus on the parameters such as the relative virtual addresses (RVA) of the functions. We have implemented our approach in Xen and conducted experiments with more than 100 malware of different types. The experiment results show that our approach can effectively detect the malware with very low increases in overhead at guest VMs.

Xie, Y., Bodala, I. P., Ong, D. C., Hsu, D., Soh, H..  2019.  Robot Capability and Intention in Trust-Based Decisions Across Tasks. 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). :39—47.

In this paper, we present results from a human-subject study designed to explore two facets of human mental models of robots - inferred capability and intention - and their relationship to overall trust and eventual decisions. In particular, we examine delegation situations characterized by uncertainty, and explore how inferred capability and intention are applied across different tasks. We develop an online survey where human participants decide whether to delegate control to a simulated UAV agent. Our study shows that human estimations of robot capability and intent correlate strongly with overall self-reported trust. However, overall trust is not independently sufficient to determine whether a human will decide to trust (delegate) a given task to a robot. Instead, our study reveals that estimations of robot intention, capability, and overall trust are integrated when deciding to delegate. From a broader perspective, these results suggest that calibrating overall trust alone is insufficient; to make correct decisions, humans need (and use) multi-faceted mental models when collaborating with robots across multiple contexts.

Xie, Yuanpeng, Jiang, Yixin, Liao, Runfa, Wen, Hong, Meng, Jiaxiao, Guo, Xiaobin, Xu, Aidong, Guan, Zewu.  2015.  User Privacy Protection for Cloud Computing Based Smart Grid. 2015 IEEE/CIC International Conference on Communications in China - Workshops (CIC/ICCC). :7–11.

The smart grid aims to improve the efficiency, reliability and safety of the electric system via modern communication system, it's necessary to utilize cloud computing to process and store the data. In fact, it's a promising paradigm to integrate smart grid into cloud computing. However, access to cloud computing system also brings data security issues. This paper focuses on the protection of user privacy in smart meter system based on data combination privacy and trusted third party. The paper demonstrates the security issues for smart grid communication system and cloud computing respectively, and illustrates the security issues for the integration. And we introduce data chunk storage and chunk relationship confusion to protect user privacy. We also propose a chunk information list system for inserting and searching data.