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A SaaS-Native Wildcard Searchable Encryption Scheme for Protecting privacy in Cloud Services 5

A SaaS-Native Wildcard Searchable Encryption Scheme for Prot...

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5th IEEE International conference on Big data Intelligence and Computing, dataCom 2019

作者： Chung, Shen-Ming Shieh, Ming-Der Chiueh, Tzi-Cker National Cheng Kung University Depart. of Electrical Engineering Tainan Taiwan Industrial Technology Research Institute Information and Comm. Research Labs Hsinchu Taiwan

ISBN: (纸本)9781728141176

As GDPR and similar regulations are taking a more important role in business, cloud services lacking privacy-protection mechanisms could suffer the most. Fortunately, some technical solutions do exist and, among them, Searchable Encryption (SE) stands out as the most efficient one to address the privacy issue while allowing services to do the jobs. However, though providing both confidentiality and searchability for the private data stored in semi-trusted environments such as cloud, SE is rarely deployed in cloud services because most SE schemes are either lacking decent query expressiveness or requiring database modifications, and thus is not native to Software-as-a-Service (SaaS) running in clouds. Considering the modification on existing databases would bring instability and even risks to cloud services, in this paper we present a wildcard SE scheme named FETCH that is able to work with off-the-shelves databases without requiring any modification. Our idea is to transform the problem of wildcard SE searching into a problem of subsequence matching. Since the transformed problem can be easily tackled by most databases, our scheme proves to fit well with SaaS in general with high efficiency. However, a caution has to be noticed that, for the outstanding performance, our scheme is actually trading off theoretical indistinguishability. therefore, in this paper security discussions are also given to elaborate both strength and weakness of the proposed FETCH. © 2019 IEEE.

关键词： Differential privacy

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application of IoT in Smart Grid: Challenges and Solutions 5

Application of IoT in Smart Grid: Challenges and Solutions

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5th Iranian conference on Signal Processing and Intelligent Systems (ICSPIS)

作者： Davoody-Beni, Zahra Sheini-Shahvand, Niloufar Shahinzadeh, Hossein Moazzami, Majid Shaneh, Mahdi Gharehpetian, Gevork B. Islamic Azad Univ Najafabad Branch Dept Elect Engn Najafabad Iran Amirkabir Univ Technol Dept Elect Engn Tehran Polytech Tehran Iran Islamic Azad Univ Smart Microgrid Res Ctr Najafabad Branch Afabad Iran

ISBN: (纸本)9781728153506

the prevailing development in energy grids and emersion of new energy players along with the advent of the Internet of things (IoT) lead available energy systems (e.g., smart electricity grid) toward "energy internet" concept. the expansion of an electrical power grid, because of its unique features, has caused this system to be converted to the central core of the energy ecosystem over the past decades. this process has also been preserved in the IoT in such a way that the presence of this technology increases the ability of evolutionary development in the power industry. IoT applications in smart grid (IoT SG) have lots of advantages such as expenditure reduction, save of time, and smartness of grid equipment. Nevertheless, the disadvantages pertaining to IoT-SG should not be neglected. For instance, one of the most significant drawbacks and ahead challenges of IoT-SG is security and big data issues. In IoT-SG, each connected device can be a probable port to the IoT's infrastructure with personal data. Concerns about security and data privacy are indispensable, but with the entrance of complexity, safety weaknesses, and probable vulnerabilities, in cases such as interoperability and autonomous decision-making, possible risks of IoT have reached new levels. this paper mostly focuses on the IoT-SG investigations, advantages and ahead challenges, plus effective solutions to these challenges are being discussed. Furthermore, according to some key challenges such as safety and big data, general conclusions for confronting and dealing with these challenges have been made.

关键词： Internet of thing Smart Grid Big data security Unit Standard Internet of Energy

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Secure and balanced scheme for non-local data storage in blockchain network 21

Secure and balanced scheme for non-local data storage in blo...

引用

21st IEEE International conference on High Performance Computing and Communications, 17th IEEE International conference on Smart City and 5th IEEE International conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Liu, Tonglai Wu, Jigang Li, Jiaxing Li, Jingyi School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Guangxi Key Laboratory of Cryptography and Information Security Guilin541004 China

ISBN: (纸本)9781728120584

Blockchain-based decentralized storage is one of the most typical and prevalence application. However, current works are not concerned about the storage imbalance caused by data leakage. this heavily impacts the availability and stability of the blockchain network. this paper proposes a double-chain scheme to improve the security of the blockchain network. the problem on storage unbalance is formalized, and a heuristic algorithm named FMA is proposed, followed by a customized genetic algorithm (GA), together with a tabu search algorithm (TSA), to deal with the problem for non-local data storage. Numerical results show that the proposed FMA can obtain the same storage fitness compared with GA and TSA for the case of the small number of nodes. GA is able to outperform the other two algorithms in terms of storage fitness, and the solution quality of TSA is bound by the qualities of FMA and GA. © 2019 IEEE.

关键词： Blockchain

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data Anonymization for privacy Aware Machine Learning 5th

Data Anonymization for Privacy Aware Machine Learning

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5th International conference on Machine Learning, Optimization, and data Science (LOD)

作者： Jaidan, David Nizar Carrere, Maxime Chemli, Zakaria Poisvert, Remi Innovat L B Scalian France Labege France Ctr Excellence Datascale Scalian France Le Haillan France Innovat L B Scalian France Paris France Innovat L B Scalian France Rennes France

ISBN: (纸本)9783030375997;9783030375980

the increase of data leaks, attacks, and other ransom-ware in the last few years have pointed out concerns about data security and privacy. All this has negatively affected the sharing and publication of data. To address these many limitations, innovative techniques are needed for protecting data. Especially, when used in machine learning based-data models. In this context, differential privacy is one of the most effective approaches to preserve privacy. However, the scope of differential privacy applications is very limited (e. g. numerical and structured data). therefore, in this study, we aim to investigate the behavior of differential privacy applied to textual data and time series. the proposed approach was evaluated by comparing two Principal Component Analysis based differential privacy algorithms. the effectiveness was demonstrated through the application of three machine learning models to both anonymized and primary data. their performances were thoroughly evaluated in terms of confidentiality, utility, scalability, and computational efficiency. the PPCA method provides a high anonymization quality at the expense of a high time-consuming, while the DPCA method preserves more utility and faster time computing. We show the possibility to combine a neural network text representation approach with differential privacy methods. We also highlighted that it is well within reach to anonymize real-world measurements data from satellites sensors for an anomaly detection task. We believe that our study will significantly motivate the use of differential privacy techniques, which can lead to more data sharing and privacy preserving.

关键词： privacy Anonymization Machine learning Text encoding Natural language processing Time series Anomaly detection

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Blockchain-Based privacy-Preserving Authentication and Message Dissemination Scheme for VANET 19

Blockchain-Based Privacy-Preserving Authentication and Messa...

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proceedings of the 2019 5th International conference on Systems, Control and Communications

作者： Bachira Guehguih Hongwei Lu School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781450372640

the vehicular ad hoc network (VANET) is an intelligent transportation system application that aims to ensure the security of road traffic information over V2V communication and V2I communication. In this paper, we propose a blockchain-based privacy-preserving authentication scheme in VANETs. We explore the strategy of a local blockchain for VANETs. We make use of a private blockchain for authentication and a public blockchain for managing event messages. the trusted authority (TA) is responsible for making the transactions in the private blockchain within the boundary of the countries, the transactions are the identity information required for authentication of vehicles when they join the network for the first time, the other vehicles have the right to read and check from the private blockchain the authenticity of a new vehicle. the public blockchain is used for storing the event messages within the boundary of pre-defined regions to ensure the security of message dissemination, we called this kind of blockchain road-side unit blockchain (RSU-BC) because it takes the role of RSU in a VANET. In this way, we eliminate the necessity of RSU's deployment in VANETs and reduce the dependency on the TA. By way of security analysis and security-proofing, we prove that our scheme satisfies multiple security and privacy requirements and can resist many attacks, while the performance analysis shows the efficiency of the scheme in terms of computation overheads and communication overheads.

关键词： Authentication Regional blockchain Blockchain privacy-preserving Vehicular ad hoc network

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data-Pattern Driven Cache-Collision Timing Attacks Against AES-GCM 5

Data-Pattern Driven Cache-Collision Timing Attacks Against A...

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5th IEEE International conference on Big data Intelligence and Computing, dataCom 2019

作者： Huang, James Li, Xiaoming University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States

ISBN: (纸本)9781728141176

data security is an important issue in Big data applications. Even though the same problem has been extensively studied within other contexts, Big data applications present several unique challenges. Among them is actually the volume of data that typical Big data applications process. Just because there is so much data passing through, the sheer data volume provides way more opportunities for a potential attacker to observe and identify patterns in computation and data. In this paper, we reveal that the data/computation patterns derived from the observation of large volume of data can be associated with certain sensitive information, specifically the key used in the AES-GCM algorithm. the AES-GCM algorithm is one of the foundation algorithms in data security, therefore such data-pattern driven attack can potentially compromise the security of Big data application. We present an attack model and an implementation of the attack based on OpenSSL, a widely used library that provides security-related functions for many applications. In most cases, our attack methodology is able to converge and extract the hidden key. We also discuss potential counter measures against similar attacks. © 2019 IEEE.

关键词： AES-GCM Cache Collision data Pattern

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BlockChain for IoT security and Management: Current Prospects, Challenges and Future Directions 5

BlockChain for IoT Security and Management: Current Prospect...

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5th International conference on Networking, Systems and security (NSysS)

作者： Roy, Shanto Ashaduzzaman, Md. Hassan, Mehedi Chowdhury, Arnab Rahman Green Univ Bangladesh Dept Comp Sci & Engn Dhaka 1207 Bangladesh

ISBN: (纸本)9781728113241

the paper presents an in detailed case study of integrating BlockChain (BC) in IoT ecosystems in order to achieve security and privacy. Since the use in cryptocurrency, BlockChain has gained tremendous attraction due to the versatile application prospects it raises. As internet of things (IoT) is emerging towards the smart city requirements, device or data security seems to be a major concern. therefore, BlockChain based decentralized and distributed system can meet the privacy preserving management in IoT ecosystems. In this paper, we discuss the necessity of utilizing BlockChain for IoT security, privacy, management. We present a literature overview of current progresses and security enhancement in sensor networks using BlockChain;their scope, prospects and limitations as well. Finally, we suggest some future directions to indicate further areas of improvement.

关键词： BlockChain IoT security privacy Authentication

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Traffic classification and application identification based on machine learning in large-scale supercomputing center 21

Traffic classification and application identification based ...

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作者： Zhao, Shilin Ye, Kejiang Xu, Cheng-Zhong Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Faculty of Science and Technology University of Macau China

ISBN: (纸本)9781728120584

Internet traffic classification and application identification associated with network traffic is an essential step for network security and traffic engineering. the traditional port-based, payload-based or statistic-based classification methods do not work well in practice. To solve the problem, in this paper, we propose a new classification model which is verified by supervised learning algorithms (e.g., RandomForest, C4.5, KNN). the model includes two main parts: a clustering flow label propagation technique based on equivalent flow-labeled propagation and a synthetic-flow feature generation algorithm based on Bidirectional-flow (BDF). To further improve the accuracy of traffic classifiers and reduce the cost, we also propose a feature selection algorithm. the experiments are done on real-world network traffic from a large-scale Supercomputing center, showing that the proposed model achieves better performance and higher accuracy for network traffic classification. © 2019 IEEE.

关键词： Network security

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5th International conference on Artificial Intelligence and security, ICAIS 2019

5th International Conference on Artificial Intelligence and ...

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5th International conference on Artificial Intelligence and security, ICAIS 2019

ISBN: (纸本)9783030242732

the proceedings contain 229 papers. the special focus in this conference is on Artificial Intelligence and security. the topics include: Detection of Microblog Overlapping Community Based on Multidimensional Information and Edge Distance Matrix;research on Settlement Deformation of Asphalt Pavement Structure in Soft Soil Zone Based on Pavement Settlement theory;legal Case Inspection: An Analogy-Based Approach to Judgment Evaluation;a Novel Noise Filter Based on Multiple Voting;fuzzy Control Method for Path Tracking System of Combine Harvester;research on Opinion Spam Detection by Time Series Anomaly Detection;application of Machine Learning Methods for Material Classification with Multi-energy X-Ray Transmission Images;Intra-class Classification of Architectural Styles Using Visualization of CNN;MSE-Net: Pedestrian Attribute Recognition Using MLSC and SE-Blocks;NOAA-LSTM: A New Method of Dialect Identification;CBAM-GAN: Generative Adversarial Networks Based on Convolutional Block Attention Module;a Novel Distributed Knowledge Reasoning Model;Research on Detection Method of Abnormal Traffic in SDN;Research on Constructing Technology of Implicit Hierarchical Topic Network Based on FP-Growth;PPD-DL: privacy-Preserving Decentralized Deep Learning;towards Edge Computing Based Distributed data Analytics Framework in Smart Grids;android Malware Identification Based on Traffic Analysis;unsupervised Traditional Chinese Medicine Text Segmentation Combined with Domain Dictionary;research on the Efficiency and application of Ship-Helicopter Cooperative Search;cellular Neural Network Based Contour Detection for Seismic Image;Evaluation Method of Teachers’ Teaching Ability Based on BP Neural Network;discovering New Sensitive Words Based on Sensitive Information Categorization;a Dynamic Event Region Tracking Approach Based on Node Calibration;a Tutorial of Graph Representation.

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Linear SVM for classifying breast cancer data encrypted using homomorphic cryptosystem 5

Linear SVM for classifying breast cancer data encrypted usin...

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5th International conference on Science and Technology, ICST 2019

作者： Sari, Anny Kartika Widya Prasetya, Faisal Malik Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9781728123691

Support Vector Machine (SVM) is widely used for data classification in many fields, including in biomedical area. While data privacy is a requirement for patients' health records, most existing classification models that are based on SVM cannot be operated on encrypted data. With the increasing use of cloud system, the need to process encrypted data cannot be avoided, especially for data stored in cloud system. this paper proposes a classification model that is based on Linear SVM. the model works on data that is encrypted using Paillier homomorphic encryption. Modification on Linear SVM classifier must be conducted to suit the requirement. Based on the evaluation, the accuracy, precision and recall of the proposed model is as good as those in the model that is applied on un-encrypted data. In terms of processing time, the application of modified Linear SVM classifier on encrypted data does not increase the time complexity. © 2019 IEEE.

关键词： Classification (of information)

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