scholarly journals Blockchain-Based Privacy-Preserving System for Genomic Data Management Using Local Differential Privacy

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3019
Author(s):  
Young-Hoon Park ◽  
Yejin Kim ◽  
Junho Shim
Keyword(s):  
Data Management ◽  
Differential Privacy ◽  
Genomic Data ◽  
Third Party ◽  
Data Management System ◽  
Sensitive Information ◽  
Healthcare Data ◽  
Large Size ◽  
Gene Data ◽  
And Control

The advances made in genome technology have resulted in significant amounts of genomic data being generated at an increasing speed. As genomic data contain various privacy-sensitive information, security schemes that protect confidentiality and control access are essential. Many security techniques have been proposed to safeguard healthcare data. However, these techniques are inadequate for genomic data management because of their large size. Additionally, privacy problems due to the sharing of gene data are yet to be addressed. In this study, we propose a secure genomic data management system using blockchain and local differential privacy (LDP). The proposed system employs two types of storage: private storage for internal staff and semi-private storage for external users. In private storage, because encrypted gene data are stored, only internal employees can access the data. Meanwhile, in semi-private storage, gene data are irreversibly modified by LDP. Through LDP, different noises are added to each section of the genomic data. Therefore, even though the third party uses or exposes the shared data, the owner’s privacy is guaranteed. Furthermore, the access control for each storage is ensured by the blockchain, and the gene owner can trace the usage and sharing status using a decentralized application in a mobile device.

scholarly journals Developing a modern data workflow for evolving data

2018 ◽  
Author(s):  
Glenda M. Yenni ◽  
Erica M. Christensen ◽  
Ellen K. Bledsoe ◽  
Sarah R. Supp ◽  
Renata M. Diaz ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Data Management ◽  
Research Process ◽  
Data Management System ◽  
Limited Attention ◽  
Data Pipeline ◽  
Evolving Data ◽  
Access To Data ◽  
And Control ◽  
Quality Assurance And Control

AbstractData management and publication are core components of the research process. An emerging challenge that has received limited attention in biology is managing, working with, and providing access to data under continual active collection. “Evolving data” present unique challenges in quality assurance and control, data publication, archiving, and reproducibility. We developed a evolving data workflow for a long-term ecological study that addresses many of the challenges associated with managing this type of data. We do this by leveraging existing tools to: 1) perform quality assurance and control; 2) import, restructure, version, and archive data; 3) rapidly publish new data in ways that ensure appropriate credit to all contributors; and 4) automate most steps in the data pipeline to reduce the time and effort required by researchers. The workflow uses two tools from software development, version control and continuous integration, to create a modern data management system that automates the pipeline.

scholarly journals Decentralizing Privacy: Protecting Personal Data by Blockchain

2019 ◽  
Vol 8 (12S) ◽  
pp. 172-175
Keyword(s):  
Access Control ◽  
Data Management ◽  
Management System ◽  
Personal Data ◽  
Third Parties ◽  
Third Party ◽  
Data Management System ◽  
Huge Amount ◽  
Security Breaches ◽  
Financial Transactions

the security of users are questioned when security breaches occur in data when third parties are incorporated for collecting and controlling huge amount of personal data. A decentralized network of peers are accompanied by a public ledger and it has demonstrated bitcoin in the financial space that trusted and auditable computing. This paper describes a decentralized personal data management system for ensuring users control over their data. A protocol is implemented that is capable of turning a blockchain into an automated access-control manager that is not requiring trust in a third party. There are no strict financial transactions in our system. They are used for carrying instructions like querying, storing and sharing data. Finally, some possible blockchain extensions are discussed that are able to harness them into a well-rounded solution for faithful computing problems in society.

Rapid Development of Multi-Source Heterogeneous Drilling Data Service System

2021 ◽  
Author(s):  
Zhong Cheng ◽  
Rongqiang Xu ◽  
Jianbing Chen ◽  
Ning Li ◽  
Xiaolong Yu ◽  
...  
Keyword(s):  
Data Management ◽  
Management System ◽  
Service System ◽  
Third Party ◽  
Data Management System ◽  
Data Service ◽  
Offshore Drilling ◽  
The Third ◽  
Drilling Data ◽  
Drilling Operations

Abstract Digital oil and gas field is an overly complex integrated information system, and with the continuous expansion of business scale and needs, oil companies will constantly raise more new and higher requirements for digital transformation. In the previous system construction, we adopted multi-phase, multi-vendor, multi-technology and multi-method, resulting in the problem of data silos and fragmentation. The result of the data management problems is that decisions are often made using incomplete information. Even when the desired data is accessible, requirements for gathering and formatting it may limit the amount of analysis performed before a timely decision must be made. Therefore, through the use of advanced computer technologies such as big data, cloud computing and IOT (internet of things), it has become our current goal to build an integrated data integration platform and provide unified data services to improve the company's bottom line. As part of the digital oilfield, offshore drilling operations is one of the potential areas where data processing and advanced analytics technology can be used to increase revenue, lower costs, and reduce risks. Building a data mining and analytics engine that uses multiple drilling data is a difficult challenge. The workflow of data processing and the timeliness of the analysis are major considerations for developing a data service solution. Most of the current analytical engines require more than one tool to have a complete system. Therefore, adopting an integrated system that combines all required tools will significantly help an organization to address the above challenges in a timely manner. This paper serves to provide a technical overview of the offshore drilling data service system currently developed and deployed. The data service system consists of four subsystems. They are the static data management system including structured data (job report) and unstructured data (design documentation and research report), the real-time data management system, the third-party software data management system integrating major industry software databases, and the cloud-based data visual application system providing dynamic analysis results to achieve timely optimization of the operations. Through a unified logical data model, it can realize the quick access to the third-party software data and application support; These subsystems are fully integrated and interact with each other to function as microservices, providing a one-stop solution for real-time drilling optimization and monitoring. This data service system has become a powerful decision support tool for the drilling operations team. The learned lessons and gained experiences from the system services presented here provide valuable guidance for future demands E&P and the industrial revolution.

Design of the Upper Computer System in the Overhead Crane Wireless Data Management System

2013 ◽  
Vol 391 ◽  
pp. 603-606
Author(s):  
Jun Fang Li ◽  
Peng Zhang ◽  
Qiang Gao ◽  
Sha Sha Chen
Keyword(s):  
Data Management ◽  
Data Management System ◽  
Wireless Data ◽  
Digital Radio ◽  
Control Center ◽  
Wireless Data Acquisition ◽  
Transmission Equipment ◽  
Visual Basic6.0 ◽  
Seamless Connection ◽  
And Control

Aiming at the problems of data messy, data unshared and thus the process control unsmooth and management efficiency in the work of crane, a wireless data acquisition system was designed based on the industrial wireless transmission equipment Nissei ND250 digital radio. To design the weighing data management software using Visual Basic6.0 combined with database, and to connect the digital radio by using MSComm, this system can realize the seamless connection of each substation and control center. In addition, to collect the various parameters into compute, it can complete the data analysis and storage. Nowadays, there is no example of the application in crane using digital radio. This method is very important to develop the technology in other areas.

scholarly journals Private Model Compression via Knowledge Distillation

2019 ◽  
Vol 33 ◽  
pp. 1190-1197 ◽  
Author(s):  
Ji Wang ◽  
Weidong Bao ◽  
Lichao Sun ◽  
Xiaomin Zhu ◽  
Bokai Cao ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Differential Privacy ◽  
Service Providers ◽  
Sample Selection ◽  
Sensitive Information ◽  
Model Compression ◽  
Complex Models ◽  
Speed Up ◽  
Knowledge Distillation ◽  
And Control

The soaring demand for intelligent mobile applications calls for deploying powerful deep neural networks (DNNs) on mobile devices. However, the outstanding performance of DNNs notoriously relies on increasingly complex models, which in turn is associated with an increase in computational expense far surpassing mobile devices’ capacity. What is worse, app service providers need to collect and utilize a large volume of users’ data, which contain sensitive information, to build the sophisticated DNN models. Directly deploying these models on public mobile devices presents prohibitive privacy risk. To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA. Following the knowledge distillation paradigm, we jointly use hint learning, distillation learning, and self learning to train a compact and fast neural network. The knowledge distilled from the cumbersome model is adaptively bounded and carefully perturbed to enforce differential privacy. We further propose an elegant query sample selection method to reduce the number of queries and control the privacy loss. A series of empirical evaluations as well as the implementation on an Android mobile device show that RONA can not only compress cumbersome models efficiently but also provide a strong privacy guarantee. For example, on SVHN, when a meaningful (9.83,10−6)-differential privacy is guaranteed, the compact model trained by RONA can obtain 20× compression ratio and 19× speed-up with merely 0.97% accuracy loss.

scholarly journals Blockchain’s potential to address issues in genomics research and how it is being used today

2020 ◽  
pp. 13-15
Author(s):  
Tony Jung ◽  
Richard Leu
Keyword(s):  
Single Point ◽  
Genomic Data ◽  
Cloud Services ◽  
Third Party ◽  
Sensitive Information ◽  
Process Data ◽  
Privacy Concerns ◽  
Genomics Research ◽  
Multiple Copies ◽  
Computational Resources

Advancements in technology have greatly decreased the costs of genome sequencing and expedited the entire sequencing process. As a result, there has been a significant increase in the volume of genomic data. Although this is useful for genomics research, there are two major concerns with this increase in data. First, the greater volume of genomic data requires a substantial amount of computational resources to process and store this data. While cloud services can seem like an effective solution to process and store this data, cloud services aggregate their information in one data center which results in the risk of a single point of failure. With the increase in genomic data, there is also an increase in privacy concerns because genomic data contains personal and sensitive information. People are not comfortable with large companies that store genomic data and people do not want this data shared with the public. Blockchain is a network that can utilize numerous computers to process data and store multiple copies of the database to eliminate the risk of a single point of failure. The blockchain is also a decentralized network which means that it is not regulated by a third party. This allows the data contributors to have full ownership of their genomic data and can decide who can access it. Today, there are several companies that have realized the advantages of blockchain and adopted this technology to store genomic data and give data contributors full control over this data.

HealthBlock: A secure blockchain-based healthcare data management system

2021 ◽  
pp. 108500
Author(s):  
Bessem Zaabar ◽  
Omar Cheikhrouhou ◽  
Faisal Jamil ◽  
Meryem Ammi ◽  
Mohamed Abid
Keyword(s):  
Data Management ◽  
Management System ◽  
Data Management System ◽  
Healthcare Data

A Healthcare Data Management System on Blockchain Framework

2021 ◽  
Author(s):  
Sandipan Saha ◽  
Asha Majumder ◽  
Tanmay Bhowmik ◽  
Abhishek Basu ◽  
Amitava Choudhury
Keyword(s):  
Data Management ◽  
Management System ◽  
Data Management System ◽  
Healthcare Data

Geotechnical Data Management System of TRANSPETRO Pipe Routes: GeoRisco

2004 ◽  
Author(s):  
Renato Seixas da Rocha ◽  
A´lvaro Maia da Costa ◽  
Cla´udio dos Santos Amaral ◽  
Ana Lu´cia Lodi da Cruz ◽  
Anderson Oliveira Soares ◽  
...  
Keyword(s):  
Data Management ◽  
Structural Integrity ◽  
Data Management System ◽  
Foundation Soil ◽  
Whole Process ◽  
Field Instrumentation ◽  
Instrument Type ◽  
Monitoring Process ◽  
The Right ◽  
And Control

The integrity management of an extensive pipeline mesh requests the acquisition, transmission, storage, analysis and control of a great amount of data. Guarantee the quality of the data in each one of those stages is of extreme importance in the execution of an effective decision process. Among several fields or disciplines that are related to the structural integrity of on-shore pipelines, the Geotechnics is receiving the right importance. More and more a buried pipeline has been considered as a geotechnical work, once the interaction of the foundation soil with the pipes has been demonstrating to be an important phenomenon to be monitored, with the objective of maintaining acceptable levels of strain and stress in the pipe. Therefore, the whole process of reading the geotechnical instrumentation installed in the piperoute domain has a great impact on the management of the pipeline integrity. As examples of that instrumentation we have: inclinometers, piezometers, strain-gages, etc. This work presents the information system and the methodology that were implanted by TRANSPETRO in the data management of its geotechnical instrumentation and that was named GeoRisco System. The GeoRisco System almost embraces all the stages of the monitoring process where geotechnical instrumentation exists. The monitoring process begins with the standardization of the readings supply. For this purpose, a program was created to generate XML files that facilitate the transmission and sharing of data on the Web. To store the readings of the field instrumentation it was implemented a database that centralizes these data and can be accessed in all the units of the Company. In order to facilitate for the technicians the analysis of these data, a computer program was created, where several types of graphs and spreadsheets were implemented for each instrument type. Finally, a program was developed to alert for critical changes in the measured variables that were selected to control the pipelines safety.

scholarly journals Random dictatorship for privacy-preserving social choice

2019 ◽  
Vol 19 (5) ◽  
pp. 537-545
Author(s):  
Vicenç Torra
Keyword(s):  
Social Choice ◽  
Differential Privacy ◽  
Third Party ◽  
Sensitive Information ◽  
Multiparty Computation ◽  
Computation Model ◽  
Decision Method ◽  
Collective Decisions ◽  
Privacy Model ◽  
Cryptographic Techniques

Abstract Social choice provides methods for collective decisions. They include methods for voting and for aggregating rankings. These methods are used in multiagent systems for similar purposes when decisions are to be made by agents. Votes and rankings are sensitive information. Because of that, privacy mechanisms are needed to avoid the disclosure of sensitive information. Cryptographic techniques can be applied in centralized environments to avoid the disclosure of sensitive information. A trusted third party can then compute the outcome. In distributed environments, we can use a secure multiparty computation approach for implementing a collective decision method. Other privacy models exist. Differential privacy and k-anonymity are two of them. They provide privacy guarantees that are complementary to multiparty computation approaches, and solutions that can be combined with the cryptographic ones, thus providing additional privacy guarantees, e.g., a differentially private multiparty computation model. In this paper, we propose the use of probabilistic social choice methods to achieve differential privacy. We use the method called random dictatorship and prove that under some circumstances differential privacy is satisfied and propose a variation that is always compliant with this privacy model. Our approach can be implemented using a centralized approach and also a decentralized approach. We briefly discuss these implementations.

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