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检索条件"任意字段=Proceedings of the 5th ACM Conference on Data and Application Security and Privacy"

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An Empirical Study of Differentially-Private Analytics for High-Speed Network data 18

An Empirical Study of Differentially-Private Analytics for H...

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8th acm conference on data and application security and privacy (CODASPY)

作者： Niculaescu, Oana-Georgiana Ghinita, Gabriel UMass Boston Boston MA 02125 USA

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Edge computing for user-centric secure search on cloud-based encrypted big data 21

Edge computing for user-centric secure search on cloud-based...

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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： Ahmad, Sahan Zobaed, S.M. Gottumukkala, Raju Salehi, Mohsen Amini School of Computing AND Informatics Louisiana70504 United States Informatics Research Institute University of Louisiana at Lafayette Lafayette Louisiana70504 United States

ISBN: (纸本)9781728120584

Cloud service providers offer a low-cost and convenient solution to host unstructured data. However, cloud services act as third-party solutions and do not provide control of the data to users. this has raised security and privacy concerns for many organizations (users) with sensitive data to utilize cloud-based solutions. User-side encryption can potentially address these concerns by establishing user-centric cloud services and granting data control to the user. Nonetheless, user-side encryption limits the ability to process (e.g., search) encrypted data on the cloud. Accordingly, in this research, we provide a framework that enables processing (in particular, searching) of encrypted multiorganizational (i.e., multi-source) big data without revealing the data to cloud provider. Our framework leverages locality feature of edge computing to offer a user-centric search ability in a realtime manner. In particular, the edge system intelligently predicts the user's search pattern and prunes the multi-source big data search space to reduce the search time. the pruning system is based on efficient sampling from the clustered big dataset on the cloud. For each cluster, the pruning system dynamically samples appropriate number of terms based on the user's search tendency, so that the cluster is optimally represented. We developed a prototype of a user-centric search system and evaluated it against multiple datasets. Experimental results demonstrate 27% improvement in the pruning quality and search accuracy. © 2019 IEEE.

关键词： Edge computing

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ClustCrypt: privacy-preserving clustering of unstructured big data in the cloud 21

ClustCrypt: Privacy-preserving clustering of unstructured bi...

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作者： Zobaed, S.M. Ahmad, Sahan Gottumukkala, Raju Salehi, Mohsen Amini School of Computing and Informatics Informatics Research Institute University of Louisiana at Lafayette Louisiana70504 United States

ISBN: (纸本)9781728120584

security and confidentiality of big data stored in the cloud are important concerns for many organizations to adopt cloud services. One common approach to address the concerns is client-side encryption where data is encrypted on the client machine before being stored in the cloud. Having encrypted data in the cloud, however, limits the ability of data clustering, which is a crucial part of many data analytics applications, such as search systems. To overcome the limitation, in this paper, we present an approach named ClustCrypt for efficient topic-based clustering of encrypted unstructured big data in the cloud. ClustCrypt dynamically estimates the optimal number of clusters based on the statistical characteristics of encrypted data. It also provides clustering approach for encrypted data. We deploy ClustCrypt within the context of a secure cloud-based semantic search system (S3BD). Experimental results obtained from evaluating ClustCrypt on three datasets demonstrate on average 60% improvement on clusters' coherency. ClustCrypt also decreases the search-time overhead by up to 78% and increases the accuracy of search results by up to 35%. © 2019 IEEE.

关键词： Big data

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A session-packets-based encrypted traffic classification using capsule neural networks 21

A session-packets-based encrypted traffic classification usi...

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作者： Cui, Susu Jiang, Bo Cai, Zhenzhen Lu, Zhigang Liu, Song Liu, Jian Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728120584

With the enhancement of network security awareness and excellent applicability of encryption protocols, identifying encrypted traffic is a critical and fundamental task for many network protection applications. Conventional port-based and deep packet inspection (DPI) approaches can't classify encrypted traffic effectively. Recent studies show that the approaches based on machine learning especially deep learning are effective for the task. However, these studies ignore the feature attributes of traffic like the location of fixed strings and change the effective features behind the traffic. In this paper, we propose a novel session-packets-based encrypted network traffic classification model using capsule neural networks (CapsNet), called SPCaps. the SPCaps introduces a twice-segmentation mechanism to dilute the interference traffic and increase the weight of effective traffic. And then it learns the spatial characteristics of encrypted traffic using CapsNet and outputs the results of encrypted traffic classification by a softmax classifier. We evaluate the proposed model for encrypted traffic classification in terms of service and application on publicly available ISCX VPN-nonVPN dataset. the experimental results demonstrate that SPCaps outperforms the state-of-the-art encrypted traffic classification approaches. © 2019 IEEE.

关键词： Network security

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Industrial IoT Smartbox for the Shop Floor 5

Industrial IoT Smartbox for the Shop Floor

引用

5th Experiment at International conference, exp.at 2019

作者： Malhao, Sergio Dionisio, Rogerio Torres, Pedro Instituto Politécnico de Castelo Branco Castelo Branco Portugal

ISBN: (纸本)9781728136370

Constant search for efficiency and productivity has led to innovation on the factory shop floor, representing an evolution of the current production systems combined with new technologies of industrial automation and information technology. this work presents an experimental demo of a smartbox for Industry 4.0 scenarios, allowing sensing, monitoring and data acquisition. We have tested two different approaches, depending on the communication protocol used for real time applications: OPC UA or MQTT. Raspberry Pi's platform act as an OPC UA server or MQTT broker, respectively. From the measurements, data stored in a cloud server can be accessed remotely with improved security and visualized from a computer dashboard. One of the conclusions that can be drawn is that both protocols allow data from the smartbox to be stored and easily monitored from a smartphone application or a computer web interface. MQTT is a good option in communications requiring very low bandwidth. However, there is a lack of suitable libraries to program alarm features for OPC UA Servers. © 2019 IEEE.

关键词： data acquisition

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Investigation of Mobile Machine Learning Models to Preserve the Effectiveness of User privacy

Investigation of Mobile Machine Learning Models to Preserve ...

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IEEE conference on Innovative Technologies in Intelligent Systems and Industrial applications (CITISIA)

作者： Danial Motahari Samrah Arif Arash Mohboubi Sabih ur Rehman School of Computing and Mathematics Charles Sturt University Australia

ISBN: (数字)9781728194370

ISBN: (纸本)9781728194387

Machine Learning (ML) has become one of the dominant technologies in the research world. It is being applied without exception in every field where automation and future predictions are required such as cyber security, computer vision, data science, search engines and various other disciplines. the application of ML in search engines creates a high risk of breaching user's privacy because this involves using data gathered from user's browsing history, purchase transactions, searching videos and queries. the user's information gathered from the search engine queries stored in computers, mobiles, other handheld devices is privately uploaded to a centralised cloud location and is then utilised in designing various ML models. As most ML models use a trained model that requires large datasets, user data gathered this way plays an important role in the development of the ML models. this however creates a significant privacy issue for individuals who may not want to reveal their personal information for ML training yet, prevention of this is beyond their access and control. In this article, we focus on the use of ML in mobile devices and address privacy concerns that can be raised by practising ML in mobile devices. the primary area of study in this research is the comparison of ML on mobile devices with ML on the cloud and figuring out its feasibility of becoming an essential ML for preserving user's privacy. Sequentially, this study first explores the need for using the ML algorithm to address privacy issues. Next, a pre-chosen ML algorithm will be tested on mobile devices and cloud to get the comparison outcome that justifies the adoption of privacy-preserving ML model on mobile devices to preserve the user's privacy.

关键词： privacy Machine learning algorithms Computational modeling Machine learning Search engines Mobile handsets data models

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5th International conference on Cryptology and Information security in Latin America, LATINCRYPT 2017

5th International Conference on Cryptology and Information S...

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5th International conference on Cryptology and Information security in Latin America, LATINCRYPT 2017

ISBN: (纸本)9783030252823

the proceedings contain 21 papers. the special focus in this conference is on Cryptology and Information security in Latin America. the topics include: Energy-efficient ARM64 cluster with cryptanalytic applications: 80 cores that do not cost you an ARM and a leg;generation of 8-bit S-boxes having almost optimal cryptographic properties using smaller 4-bit S-boxes and finite field multiplication;XHX - A framework for optimally secure tweakable block ciphers from classical block ciphers and universal hashing;Improved XKX-based AEAD scheme: Removing the birthday terms;aggregation of time-series data under differential privacy;the oblivious machine: Or: How to put the C into MPC;concrete efficiency improvements for multiparty garbling with an honest majority;homomorphic rank sort using surrogate polynomials;on trees, chains and fast transactions in the blockchain;Using level-1 homomorphic encryption to improve threshold DSA signatures for bitcoin wallet security;anonymous single-round server-aided verification;environmental authentication in Malware;threshold kleptographic attacks on discrete logarithm based signatures;Secure channels and termination: the last word on TLS;improved security notions for proxy re-encryption to enforce access control;optimal 2-3 chains for scalar multiplication;curve25519 for the cortex-M4 and beyond;Implementing the newhope-simple key exchange on low-cost FPGAs;theoretical security evaluation against side-channel cube attack with key enumeration.

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A real-time identification system for voip traffic in large-scale networks 21

A real-time identification system for voip traffic in large-...

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作者： Wang, Rongkang Zhang, Jianzhong Zhang, Yu Yin, Mingxin Xu, Jingdong Laboratory of Network and Information Security College of Cyber Science Nankai University Tianjin China

ISBN: (纸本)9781728120584

Recently, VoIP applications win most of the users' favor with their high service quality and low price. However, owing to the flexible of VoIP services, they will cause incalculable damage if these services are used improperly. In order to make VoIP applications serve humans better, it is important to keep VoIP applications under supervision. the upgrading of VoIP technology makes the traditional identification methods inefficient, it becomes more difficult to identify VoIP traffic. For the challenge of identifying VoIP traffic, the deep learning technology provides us a new solution. With supervised learning method, we can build feature set for identifying VoIP traffic. this way is not only able to find the most useful features, but also it can get rid of human effort in exploring features. In this paper, we adopt CLNN(Convolutional Neural Networks, Long Short-Term Memory) to extract features for accurate VoIP application identification. In addition, we design a real-time identification system to capture VoIP traffic in a large-scale network and identify their application types with the features we trained. the evaluation results verify that our system can identify VoIP traffic timely and accurately. © 2019 IEEE.

关键词： Long short-term memory

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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)9783030242671

the proceedings contain 229 papers. the special focus in this conference is on Artificial Intelligence and security. the topics include: Multi-function Quantum Cryptography Protocol Based on Bell State;the Attack Case of ECDSA on Blockchain Based on Improved Simple Power Analysis;a Weight-Based Channel Estimation Implementation Algorithm;multi-party Quantum Communication Complexity on Composite Boolean-Valued Function;BlockZone: A Blockchain-Based DNS Storage and Retrieval Scheme;robust Analysis of Grid System Based on Complex Network Attack Mode;A Secure data Aggregation Protocol in VANETs Based on Multi-key FHE;Research on SQL Injection and Defense Technology;a Review of the Factorization Problem of Large Integers;quantum Network Coding Based on Entanglement Distribution;A Comparison of Machine Learning Algorithms for Detecting XSS Attacks;a Survey of privacy-Preserving Techniques for Blockchain;a Survey of Software Reverse Engineering applications;A Fast IP Matching Algorithm Under Large Traffic;An Effective Load Balance Using Link Bandwidth for SDN-Based data Centers;zero-Day Vulnerability Risk Assessment and Attack Path Analysis Using security Metric;research on Content Extraction of Rich Text Web Pages;Network Protocol Analysis Base on WeChat PC Version;bitcoin Network Size Estimation Based on Coupon Collection Model;a Survey of the Software Vulnerability Discovery Using Machine Learning Techniques;Moving Target Defense in Preventing SQL Injection;a Distributed Cryptanalysis Framework Based on Mobile Phones;a Novel threshold Signature Scheme Based on Elliptic Curve with Designated Verifier;A Congestion Control Methodology with Probability Routing Based on MNL for datacenter Network;fast Failover for Link Failures in Software Defined Networks.

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5th International conference on Soft Computing in data Science, SCDS 2019

5th International Conference on Soft Computing in Data Scien...

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5th International conference on Soft Computing in data Science, SCDS 2019

ISBN: (纸本)9789811503986

the proceedings contain 30 papers. the special focus in this conference is on International conference on Soft Computing in data Science. the topics include: Predictive Model of Graduate-On-Time Using Machine Learning Algorithms;New Hybrid Statistical Method and Machine Learning for PM10 Prediction;b-Spline in the Cox Regression with application to Cervical Cancer;multilevel Logistic Regression and Neural Network-Genetic Algorithm for Modeling Internet Access;a Case Study on Student Attrition Prediction in Higher Education Using data Mining Techniques;the Use of Hybrid Information Retrieve Technique and Bayesian Relevance Feedback Classification on Clinical dataset;An Experience Report on Building a Big data Analytics Framework Using Cloudera CDH and RapidMiner Radoop with a Cluster of Commodity Computers;multi-stage Clustering Algorithm for Energy Optimization in Wireless Sensor Networks;case Based Reasoning for Diagnosing Types of Mental Disorders and their Treatments;decision Tree: Customer Churn Analysis for a Loyalty Program Using data Mining Algorithm;Study of Score Fusion and Quality Weighting in the Bio-Secure DS2 database;arabic Phonemes Recognition Using Convolutional Neural Network;Forecasting the Search Trend of Muslim Clothing in Indonesia on Google Trends data Using ARIMAX and Neural Network;convolutional Neural Network application in Smart Farming;Comparison of Artificial Neural Network (ANN) and Other Imputation Methods in Estimating Missing Rainfall data at Kuantan Station;anonymized User Linkage Under Differential privacy;context Enrichment Model Based Framework for Sentiment Analysis;2019 thai General Election: A Twitter Analysis;science Lab Repository Requirements Elicitation Based on Text Analytics;malaysian Budget Visualization Using Circle Packing.

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