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Optimized clustering with data privacy for wireless sensor network data transmission 5

Optimized clustering with data privacy for wireless sensor n...

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5th International conference on Intelligent Computing and Control Systems, ICICCS 2021

作者： Mohanty, Sipra Sahoo, Bhaswati School of Computer Engineering KIIT Deemed to Be University Bhubaneshwar Odisha India

ISBN: (纸本)9781665412728

the technological advancement in all fields like communication, medicine, industry, etc. demands high data transmission. data transmission is carried via different wireless sensor networks (WSN). Wireless sensor networks are low-cost, low-energy, and small multifunctional sensors that are used for dense object tracking, environmental monitoring, and industrial control. In recent times, several researchers have been drawn to sensor networks due to their feasibility and efficiency to protect data during transmission. the wireless sensor network transmits the data which is collected by a device and sent to the base station and the user. the nodes are grouped as clusters and the data are aggregated and transmitted from the cluster to the base station. Such larger collections of homogeneous or heterogeneous sensors are networked, which needs an unattended operation in various applications. this forms a wireless sensor network with several sensor nodes with the capability y of communicating with other nodes or with other base stations. the original data should be sent without loss to attackers and the base station. the large collection of sensor nodes is capable of sensing larger regions with much higher accuracy. As the size of the sensor network increases, the area of coverage increases. To improve the capacity of sensor node batteries and to reduce the consumption of energy innetwork, the idea of Compressive Sensing (CS) is used in wireless sensor networks. the Compressive Sensing theory is combined with wireless sensor networks to reduce the total data transmitted. It reduces well the consumption of energy in wireless sensor networks, possibly increases the lifetime of the network. the proposed research work provides an optimized clustering technique using compressive sensing like Tree Cluster data Gathering Compressive Sensing Algorithm (TCDGCSA) for the security of the data protection transmitted from the nodes in the cluster to the base station. © 2021 IEEE.

关键词： Compressed sensing

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Academic Student Progression Predictive Analysis application in Secondary Education Institution 2021

Academic Student Progression Predictive Analysis Application...

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5th International conference on E-Society, E-Education and E-Technology, ICSET 2021

作者： Abisado, Mideth Vargas, Peejay De Los Trinos, Ma.Ian College of Computing and Information Technologies National University Manila Philippines

ISBN: (纸本)9781450390156

the study developed an Academic Student Progression Predictive Analysis application for Dr. Josefa Jara Martinez High School (DJJMHS) to provide an intranet platform for the centralized collection of data records and management of school forms. the system includes school details, school forms, real-time records of attendance, student academic progress, prediction, visualization, monitoring of employee performance, and report generation. It has enhanced security protection and can provide all users with a secured network connection. the system uses a graphical user interface to display all the functions to access all system features. the system was developed using standards and tools such as HTML5, CSS3, JavaScript, and Phyton-MySQL. It creates an interface of system transactions in utilizing technology issuance of School Forms such as SF1, SF2, SF4, SF5, SF6, SF9, and SF10. the information can be encoded once a database connection is established. It is ready to create records into the database tables using the execute method of the developed system. All required features signified functionality and reliability based on test cases performed in two cycles in a live environment. the system was evaluated "Very Good"by 30 respondents using the ISO 25010 Software Quality Matrix. this indicates that the system can be an accurate and reliable tool for accessing an end-user's data and other services. © 2021 acm.

关键词： Students

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Detect Darknet URL Based on Artificial Neural Network 21

Detect Darknet URL Based on Artificial Neural Network

引用

5th International conference on Computer Science and application Engineering, CSAE 2021

作者： Xu, Jie Ju, Ao Jiangsu Police Institute Jiangsu Nanjing China

ISBN: (纸本)9781450389853

Darknet is a network that transmits data on the Internet through anonymous network technology and protects the relationship between the two sides of communication from being leaked. Because the IP addresses of both sides of the communication cannot be traced on the darknet, the identity of the user cannot be determined. the darknet is used by criminals to engage in criminal activities. this paper studies the URL address of the darknet, proposes an algorithm for darknet URL recognition using artificial neural network. the algorithm transforms URL into a fixed length vector, and then uses it as a part of the input data of artificial neural network for learning and classification. Experiments show that the proposed algorithm has high accuracy, can accurately identify the darknet URL through multiple iterations under different attribute accuracy. Experimental results show that the proposed algorithm can achieve 99.3% detection accuracy.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Network security

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IoT Expunge: Implementing Verifiable Retention of IoT data 20

IoT Expunge: Implementing Verifiable Retention of IoT Data

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10th acm conference on data and application security and privacy (CODASPY)

作者： Panwar, Nisha Sharma, Shantanu Gupta, Peeyush Ghosh, Dhrubajyoti Mehrotra, Sharad Venkatasubramanian, Nalini Augusta Univ Augusta GA 30912 USA Univ Calif Irvine Irvine CA 92717 USA

ISBN: (纸本)9781450371070

the growing deployment of Internet of things (IoT) systems aims to ease the daily life of end-users by providing several value-added services. However, IoT systems may capture and store sensitive, personal data about individuals in the cloud, thereby jeopardizing user-privacy. Emerging legislation, such as California's CalOPPA and GDPR in Europe, support strong privacy laws to protect an individual's data in the cloud. One such law relates to strict enforcement of data retention policies. this paper proposes a framework, entitled IoT Expunge that allows sensor data providers to store the data in cloud platforms that will ensure enforcement of retention policies. Additionally, the cloud provider produces verifiable proofs of its adherence to the retention policies. Experimental results on a real-world smart building testbed show that IoT Expunge imposes minimal overheads to the user to verify the data against data retention policies.

关键词： Internet of things smart building user privacy data deletion verification

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CREPE: A privacy-Enhanced Crash Reporting System 20

CREPE: A Privacy-Enhanced Crash Reporting System

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10th acm conference on data and application security and privacy (CODASPY)

作者： Satvat, Kiavash Shirvanian, Maliheh Hosseini, Mahshid Saxena, Nitesh Univ Illinois Chicago IL 60680 USA Visa Res Foster City CA USA Univ Alabama Birmingham Birmingham AL USA

ISBN: (纸本)9781450371070

Software crashes are nearly impossible to avoid. the reported crashes often contain useful information assisting developers in finding the root cause of the crash. However, crash reports may carry sensitive and private information about the users and their systems, which may be used by an attacker who has compromised the crash reporting system to violate the user's privacy and security. Besides, a single bug may trigger loads of identical reports which excessively consumes system resources and overwhelms application developers. In this paper, we introduce CREPE, a security-concerned crash reporting solution, that effectively reduces the number of submitted crash reports to mitigate the security and privacy risk associated with the current implementation of the crash reporting system. Similar to the currently deployed systems, CREPE aggregates and categorizes the crashes based on their root cause. On top of that, the server marks the crash categories in which sufficient reports have been received as "saturated" and informs the clients periodically through software updates. On the client, CREPE engages the reporting application in categorizing each crash to only submit reports belonging to non-saturated categories. We evaluate CREPE using one year of data from Mozilla crash reporting system containing 38,834,383 reports of Firefox crashes. Our analysis suggests that we can significantly reduce the number of submitted reports by bucketing 100 most frequent crash signatures at the client. this helps to preserve the security and the privacy of a significant portion of users whose data has not been shared with the server due to the redundancy of their crash data with previously submitted reports.

关键词： Crash Reporting Crash Report privacy Crash Report security Browser privacy

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Design of Korean Text Resource Information Mining and Management Platform 5

Design of Korean Text Resource Information Mining and Manage...

引用

5th International conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Wang, Chunying College of Humanities and Foreign Languages Zhejiang Shuren University Zhejiang Hangzhou310015 China

ISBN: (纸本)9781665435246

this paper applies data mining technology to search for Korean/Korean web pages. After a brief introduction to data mining and information management technology, it mainly studies Korean word segmentation technology, topic prediction, and tunnel crossing strategy. the detailed work is as follows: the word segmentation task is the basis of natural language processing. the first task for Chinese, Japanese, Korean and other characters in various tasks is word segmentation. In terms of Korean/Korean word segmentation, traditional matching-based word segmentation methods often require manual construction of a large dictionary, which does not have the ability to recognize words that are not in the dictionary, and the accuracy of word segmentation is not very high. © 2021 IEEE.

关键词： data mining

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A Baseline for Attribute Disclosure Risk in Synthetic data 20

A Baseline for Attribute Disclosure Risk in Synthetic Data

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10th acm conference on data and application security and privacy (CODASPY)

作者： Hittmeir, Markus Mayer, Rudolf Ekelhart, Andreas SBA Res Vienna Austria

ISBN: (纸本)9781450371070

the generation of synthetic data is widely considered as viable method for alleviating privacy concerns and for reducing identification and attribute disclosure risk in micro-data. the records in a synthetic dataset are artificially created and thus do not directly relate to individuals in the original data in terms of a 1-to-1 correspondence. As a result, inferences about said individuals appear to be infeasible and, simultaneously, the utility of the data may be kept at a high level. In this paper, we challenge this belief by interpreting the standard attacker model for attribute disclosure as classification problem. We show how disclosure risk measures presented in recent publications may be compared to or even be reformulated as machine learning classification models. Our overall goal is to empirically analyze attribute disclosure risk in synthetic data and to discuss its close relationship to data utility. Moreover, we improve the baseline for attribute disclosure risk from the attacker's perspective by applying variants of the RadiusNearestNeighbor and the EnsembleVote classifiers.

关键词： privacy-Preserving data Mining Synthetic data Machine Learning

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Design and application of PLC control system based on neural network

Design and application of PLC control system based on neural...

引用

5th International conference on Machine Learning and Big data Analytics for IoT security and privacy, SPIoT 2024

作者： Zhang, Xiaoxia College of Electrical Engineering Gansu Institute of Mechanical & Electrical Engineering Tianshui741001 China

In order to better break through the performance bottleneck of traditional PLC in nonlinear temperature control system, this study proposes an incremental PID control algorithm based on BP neural network optimization. By constructing a dual model architecture of transfer function and deep learning, a 3-9-3 topological neural network is designed to dynamically adjust PID parameters to achieve collaborative optimization of proportion, integral and differential coefficients. the experimental system uses Siemens S7-1214 PLC to build a four-level control architecture, integrates TIA Portal development environment and modular programming strategy, and realizes the whole process without fluctuation control under the condition of ambient temperature 32°C and target temperature 65°C. the simulation results show that after 2.9 minutes of gradual adjustment, the proposed algorithm converges the steady-state error from the initial 3.8% to 0%, and the overshoot maintains the theoretical zero value. In this study, the backpropagation mechanism is innovatively integrated with incremental PID, and the dynamic correction of the control output is realized through the feedforward network interface module (FB1). the contradiction between overshoot suppression and speed regulation of large inertia system is solved. the research results also fully verify the deep collaborative breakthrough of neural network and classical control in time-delay systems, which has important promotion value for the accurate temperature control of intelligent manufacturing equipment. © 2025 the Authors. Published by Elsevier B.V.

关键词： three term control systems

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5th International Symposium on Cyber security Cryptography and Machine Learning, CSCML 2021

5th International Symposium on Cyber Security Cryptography a...

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5th International Symposium on Cyber security Cryptography and Machine Learning, CSCML 2021

ISBN: (纸本)9783030780852

the proceedings contain 36 papers. the special focus in this conference is on Cyber security Cryptography and Machine Learning. the topics include: Implementing GDPR in Social Networks Using Trust and Context;Principal Component Analysis Using CKKS Homomorphic Scheme;depthStAr: Deep Strange Arguments Detection;robust Multivariate Anomaly-Based Intrusion Detection System for Cyber-Physical Systems;privacy-Preserving Password Strength Meters with FHE;automatic Detection of Water Stress in Corn Using Image Processing and Deep Learning;tortoise and Hares Consensus: the Meshcash Framework for Incentive-Compatible, Scalable Cryptocurrencies;preface;programmable Bootstrapping Enables Efficient Homomorphic Inference of Deep Neural Networks;game of Drones - Detecting Spying Drones Using Time Domain Analysis;privacy Vulnerability of NeNDS Collaborative Filtering;Lawful Interception in WebRTC Peer-To-Peer Communication;hierarchical Ring Signatures Immune to Randomness Injection Attacks;theoretical Aspects of a Priori On-Line Assessment of data Predictability in Applied Tasks;Randomly Rotate Qubits, Compute and Reverse for Weak Measurements Resilient QKD and Securing Entanglement: (Extended Abstract);warped Input Gaussian Processes for Time Series Forecasting;history Binding Signature: (Extended Abstract);effective Enumeration of Infinitely Many Programs that Evade Formal Malware Analysis;DNS-Morph: UDP-Based Bootstrapping Protocol for Tor;Adversaries Strike Hard: Adversarial Attacks Against Malware Classifiers Using Dynamic API Calls as Features;polynomial Time k-Shortest Multi-criteria Prioritized and All-Criteria-Disjoint Paths: (Extended Abstract);Binding BIKE Errors to a Key Pair;fast and Error-Free Negacyclic Integer Convolution Using Extended Fourier Transform;efficient Secure Ridge Regression from Randomized Gaussian Elimination;PolyDNN Polynomial Representation of NN for Communication-Less SMPC Inference;use of Blockchain for Ensuring data Integrity in Cloud databases.

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privacyFL: A Simulator for privacy-Preserving and Secure Federated Learning 20

PrivacyFL: A Simulator for Privacy-Preserving and Secure Fed...

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29th acm International conference on Information and Knowledge Management (CIKM)

作者： Mugunthan, Vaikkunth Peraire-Bueno, Anton Kagal, Lalana MIT5 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781450368599

Federated learning is a technique that enables distributed clients to collaboratively learn a shared machine learning model without sharing their training data. this reduces data privacy risks, however, privacy concerns still exist since it is possible to leak information about the training dataset from the trained model's weights or parameters. therefore, it is important to develop federated learning algorithms that train highly accurate models in a privacy-preserving manner. Setting up a federated learning environment, especially with security and privacy guarantees, is a time-consuming process with numerous configurations and parameters that can be manipulated. In order to help clients ensure that collaboration is feasible and to check that it improves their model accuracy, a real-world simulator for privacy-preserving and secure federated learning is required. In this paper, we introduce privacyFL, which is an extensible, easily configurable, and scalable simulator for federated learning environments. Its key features include latency simulation, robustness to client departure/failure, support for both centralized (with one or more servers) and decentralized (serverless) learning, and configurable privacy and security mechanisms based on differential privacy and secure multiparty computation (MPC). In this paper, we motivate our research, describe the architecture of the simulator and associated protocols, and discuss its evaluation in numerous scenarios that highlight its wide range of functionality and its advantages. Our paper addresses a significant real-world problem: checking the feasibility of participating in a federated learning environment under a variety of circumstances. It also has a strong practical impact because organizations such as hospitals, banks, and research institutes, which have large amounts of sensitive data and would like to collaborate, would greatly benefit from having a system that enables them to do so in a privacy-preserving and se

关键词： Federated Learning Differential privacy privacy-Preserving Federated Learning Secure Multiparty Computation

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