scholarly journals FPETD: Fault-Tolerant and Privacy-Preserving Electricity Theft Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Siliang Dong ◽  
Zhixin Zeng ◽  
Yining Liu
Keyword(s):  
Machine Learning ◽  
Data Privacy ◽  
Fault Tolerant ◽  
Privacy Preserving ◽  
User Privacy ◽  
Accuracy Rate ◽  
Electricity Theft ◽  
User Data ◽  
Dimensionality Reduction Method ◽  
Better Than

Electricity theft occurs from time to time in the smart grid, which can cause great losses to the power supplier, so it is necessary to prevent the occurrence of electricity theft. Using machine learning as an electricity theft detection tool can quickly lock participants suspected of electricity theft; however, directly publishing user data to the detector for machine learning-based detection may expose user privacy. In this paper, we propose a real-time fault-tolerant and privacy-preserving electricity theft detection (FPETD) scheme that combines n -source anonymity and a convolutional neural network (CNN). In our scheme, we designed a fault-tolerant raw data collection protocol to collect electricity data and cut off the correspondence between users and their data, thereby ensuring the fault tolerance and data privacy during the electricity theft detection process. Experiments have proven that our dimensionality reduction method makes our model have an accuracy rate of 92.86% for detecting electricity theft, which is much better than others.

A Generic Privacy Breach Preventing Methodology for Cloud Based Web Service

2015 ◽  
pp. 426-458 ◽  
Author(s):  
S. R. Murugaiyan ◽  
D. Chandramohan ◽  
T. Vengattaraman ◽  
P. Dhavachelvan
Keyword(s):  
Private Information ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Digital Data ◽  
Cloud Data ◽  
Privacy Breach ◽  
Cloud Data Storage ◽  
User Data ◽  
Information Technology Development ◽  
User Data Privacy

The present focuses on the Cloud storage services are having a critical issue in handling the user's private information and its confidentiality. The User data privacy preserving is a vital facet of online storage in cloud computing. The information in cloud data storage is underneath, staid molests of baffling addict endeavor, and it may leads to user clandestine in a roar privacy breach. Moreover, privacy preservation is an indeed research pasture in contemporary information technology development. Preserving User Data in Cloud Service (PUDCS) happens due to the data privacy breach results to a rhythmic way of intruding high confidential digital storage area and barter those information into business by embezzle others information. This paper focuses on preventing (hush-hush) digital data using the proposed privacy preserving framework. It also describes the prevention of stored data and de-identifying unauthorized user attempts, log monitoring and maintaining it in the cloud for promoting allusion to providers and users.

scholarly journals FG-Droid: Grouping Based Feature Size Reduction for Android Malware Detection through Machine Learning

2021 ◽  
Author(s):  
Recep Sinan ARSLAN
Keyword(s):  
Machine Learning ◽  
Static Analysis ◽  
Processing Time ◽  
High Accuracy ◽  
User Privacy ◽  
Accuracy Rate ◽  
Feature Size ◽  
Android Malware ◽  
Android Malware Detection ◽  
Android Os

Abstract The number of applications prepared for use on mobile devices has increased rapidly with the widespread use of the Android OS. This has resulted in the undesired installation of Android apks that violate user privacy or malicious. The increasing similarity between Android malware and benign applications makes it difficult to distinguish them from each other and causes a situation of concern for users. In this study, FG-Droid, a machine-learning based classifier with an efficient working system, using the method of grouping the features obtained by static analysis, was proposed. It was created as a result of experiments with Machine learning (ML), DNN, RNN, LSTM and GRU based models using Drebin, Genome and Arslan datasets. Experimental results reveal that FG-Droid has achieved 97.7% AUC score with a vector includes only 11 static features, and ExtraTree algorithm. FG-Droid analyze the applications with using the least number of features compare to previous studies, and required the least processing time for training and prediction. As a result, it has been shown that Android malware can be detected in high accuracy rate with an effective feature set and there is no need to use a large number of features extracted with different techniques (static, dynamic or hybrid).

scholarly journals Analisis privasi data pengguna contact tracing application pengendalian COVID-19 di Indonesia berdasarkan PERPRES RI No. 95 tahun 2018 tentang sistem pemerintahan berbasis elektronik

Teknologi ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 46-58
Author(s):  
Syifa Ilma Nabila Suwandi ◽  
◽  
Xavier Wahyuadi Seloatmodjo ◽  
Alexandra Situmorang ◽  
Nur Aini Rakhmawati ◽  
...  
Keyword(s):  
Data Privacy ◽  
Continuous System ◽  
Contact Tracing ◽  
Privacy Policy ◽  
User Privacy ◽  
Literature Study ◽  
Privacy Policies ◽  
Three Samples ◽  
User Data ◽  
The Republic

The presence of user contact applications in the community as a means of preventing and overcoming the spread of COVID-19 can pose another risk to the potential dangers of protecting data privacy from contact tracing. This research examines more deeply related to user privacy policies through 3 (three) samples of android-based user contact applications that are used as a means of preventing, overcoming and controlling the spread of the COVID-19 virus in today's society and by reviewing the rules contained in the Presidential Regulation of the Republic. Indonesian No. 95 of 2018 concerning Electronic-Based Government Systems (SPBE). The study in this study was prepared using the method of literature study, observation and qualitative analysis. A comparison was made regarding the data privacy of the three samples, which was then evaluated and matched with the form of the privacy policy according to Presidential Regulation No. 95 of 2018 concerning Electronic-Based Government Systems (SPBE) and according to the ideal form of data privacy policy based on several experts. Comparative data is obtained through related applications and other electronic media which are then discussed together to conclude and evaluate the data privacy policies of the three sample applications. Based on this research, it can be concluded that privacy intervention to deal with damage and save lives is legal as long as its use is in accordance with regulations in the health, disaster, telecommunications, informatics and other related fields; in this case listed in the Presidential Decree No. 95 of 2018 concerning Electronic-Based Government Systems (SPBE) and there needs to be an increase in efforts to maintain the security and confidentiality of user data privacy through continuous system and data maintenance, encryption of data privacy storage in the manager's data warehouse and added with other data privacy policies can guarantee the security and confidentiality of the privacy of user data.

Application of Machine Learning Algorithms to a Well Defined Clinical Problem: Liver Disease

2017 ◽  
Vol 8 (4) ◽  
pp. 38-60 ◽  
Author(s):  
Sakshi Takkar ◽  
Aman Singh ◽  
Babita Pandey
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Clinical Problem ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Integrated Models ◽  
Major Health ◽  
And Performance ◽  
Dimensionality Reduction Method ◽  
Better Than

Liver diseases represent a major health burden worldwide. Machine learning (ML) algorithms have been extensively used to diagnose liver disease. This study accordingly aims to employ various individual and integrated ML algorithms on distinct liver disease datasets for evaluating the diagnostic performances, to integrate dimensionality reduction method with the ML algorithms for analyzing variation in results, to find the best classification model and to analyze the merits and demerits of these algorithms. KNN and PCA-KNN emerged to be the top individual and integrated models. The study also concluded that one specific algorithm can't show best results for all types of datasets and integrated models not always perform better than the individuals. It is observed that no algorithm is perfect and performance of an algorithm totally depends on the dataset type and structure, its number of observations, its dimensions and the decision boundary.

scholarly journals Privacy-Aware Data Forensics of VRUs Using Machine Learning and Big Data Analytics

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Muhammad Babar ◽  
Muhammad Usman Tariq ◽  
Ahmed S. Almasoud ◽  
Mohammad Dahman Alshehri
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Traffic Control ◽  
Data Analytics ◽  
Data Privacy ◽  
Big Data Analytics ◽  
Processing Unit ◽  
Privacy And Security ◽  
User Data ◽  
Data Ingestion

The present spreading out of big data found the realization of AI and machine learning. With the rise of big data and machine learning, the idea of improving accuracy and enhancing the efficacy of AI applications is also gaining prominence. Machine learning solutions provide improved guard safety in hazardous traffic circumstances in the context of traffic applications. The existing architectures have various challenges, where data privacy is the foremost challenge for vulnerable road users (VRUs). The key reason for failure in traffic control for pedestrians is flawed in the privacy handling of the users. The user data are at risk and are prone to several privacy and security gaps. If an invader succeeds to infiltrate the setup, exposed data can be malevolently influenced, contrived, and misrepresented for illegitimate drives. In this study, an architecture is proposed based on machine learning to analyze and process big data efficiently in a secure environment. The proposed model considers the privacy of users during big data processing. The proposed architecture is a layered framework with a parallel and distributed module using machine learning on big data to achieve secure big data analytics. The proposed architecture designs a distinct unit for privacy management using a machine learning classifier. A stream processing unit is also integrated with the architecture to process the information. The proposed system is apprehended using real-time datasets from various sources and experimentally tested with reliable datasets that disclose the effectiveness of the proposed architecture. The data ingestion results are also highlighted along with training and validation results.

scholarly journals A Privacy-Preserving Blockchain Supervision Framework in the Multiparty Setting

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Baodong Wen ◽  
Yujue Wang ◽  
Yong Ding ◽  
Haibin Zheng ◽  
Hai Liang ◽  
...  
Keyword(s):  
Theoretical Analysis ◽  
Privacy Protection ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
User Privacy ◽  
Supervision System ◽  
Transaction Data ◽  
Massive Growth ◽  
Attribute Based Encryption ◽  
User Data

Data supervision is an effective method to ensure the legality of user data on blockchain. However, the massive growth of data makes it difficult to achieve data supervision in existing blockchain applications. Also, data supervision often leads to problems such as disclosure of transaction data and user privacy information. To address these issues, this paper proposes a privacy-preserving blockchain supervision system (BSS) in the multiparty setting, where a supervision chain is introduced to realize data supervision on blockchain. All sensitive information such as user information in the supervising data is encrypted by the attribute-based encryption (ABE) technology, so that both privacy protection and access control on user data can be achieved. Theoretical analysis and comparison show that the proposed BSS scheme is efficient, and experimental analysis indicates the practicality of our BSS scheme.

Measuring data privacy preserving and machine learning

2018 ◽  
Author(s):  
Luis Gustavo Esquivel-Quiros ◽  
Elena Gabriela Barrantes ◽  
Fernando Esponda Darlington
Keyword(s):  
Machine Learning ◽  
Data Privacy ◽  
Privacy Preserving

Design Preference Prediction With Data Privacy Safeguards: A Preliminary Study

2017 ◽  
Author(s):  
Alexander Burnap ◽  
Panos Y. Papalambros
Keyword(s):  
Data Privacy ◽  
Personal Data ◽  
Population Level ◽  
Smart Device ◽  
Design Development ◽  
User Privacy ◽  
User Data ◽  
Preference Models ◽  
Design Preference ◽  
Device Use

Design preference models are used widely in product planning and design development. Their prediction accuracy requires large amounts of personal user data including purchase and other personal choice records. With increased Internet and smart device use, sources of personal data are becoming more varied and their capture more ubiquitous. This situation leads to questioning whether there is a trade off between improving products and compromising individual user privacy. To advance this conversation, we analyze how privacy safeguards may affect design preference modeling. We conduct an experiment using real user data to study the performance of design preference models under different levels of privacy. Results indicate there is a tradeoff between accuracy and privacy. However, with enough data, models with privacy safeguards can still be sufficiently accurate to answer population-level design questions.

scholarly journals Fault-Tolerant Privacy-Preserving Data Aggregation for Smart Grid

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Huadong Liu ◽  
Tianlong Gu ◽  
Yining Liu ◽  
Jingcheng Song ◽  
Zhixin Zeng
Keyword(s):  
Fault Tolerance ◽  
Secret Sharing ◽  
Data Aggregation ◽  
Smart Grids ◽  
Data Privacy ◽  
High Efficiency ◽  
Differential Privacy ◽  
Fault Tolerant ◽  
Privacy Preserving ◽  
Data Usability

In smart grids (SG), data aggregation is widely used to strike a balance between data usability and privacy protection. The fault tolerance is an important requirement to improve the robustness of data aggregation protocols, which enables normal execution of the protocols even with failures on some entities. However, to achieve fault tolerance, most schemes either sacrifice the aggregation accuracy due to the use of differential privacy or substitution strategy or need to rely on an online trusted entity to manage all user blinding factors. In this paper, a (k,n) threshold privacy-preserving data aggregation scheme named (k,n)-PDA is proposed, which reconciles data usability and data privacy through the BGN cryptosystem and achieves fault tolerance with accurate aggregation using Shamir’s secret sharing without any online trusted entity. Besides, our scheme supports the efficient changing of users’ membership. Specifically, the dynamic secrete key is distributed to n smart meters (SMs) through the threshold secret sharing algorithm. When k or more meters participate in the aggregation, the data service center (DSC) can reconstruct the key to compute the aggregate results, and less than k SMs cannot recover the key. Thus, our solution still works functionally even if up to n−k SMs fail; also, it resists attacks from the collusion of less than k SMs. Moreover, system and performance analyses demonstrate that our scheme achieves privacy, fault tolerance, and membership dynamics with high efficiency.

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