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CGAPP: A continuous group authentication privacy-preserving platform for industrial scene

In Industry 4.0, security begins with the workers’ authentication, which can be done individually or in groups. Recently, group authentication is gaining momentum, allowing users to authenticate as group members without the need to specify the particular individual. Continuous authentication and federated learning are promising techniques that might help group authentication by providing privacy, by its own design, and extra security compared to traditional methods based on passwords, tokens, or biometrics. However, these techniques have not previously been combined or evaluated for authenticating workers in Industry 4.0. Thus, this paper proposes a novel continuous group authentication privacy-preserving (CGAPP)platform that is suitable for the industry. The CGAPP platform incorporates statistical data from workers’ smartphones and employs federated learning-based outlier detection for group worker authentication while ensuring the privacy of personal data vectors. A series of experiments were performed to measure the framework’s suitability and address the following research questions: (i) What is the cost of using FL compared to full data access in industrial scenarios? (ii) How robust is federated learning against adversarial attacks, specifically, how much malicious data is required to deceive the model? and (iii) How much noise is required to disrupt the authentication system? The results demonstrate the effectiveness of the CGAPP platform in the industry since it provides factory safety while preserving privacy. This platform achieves an accuracy of 92%, comparable to the 96% obtained by traditional approaches in the literature that do not address privacy concerns. The platform’s robustness is tested against attacks in the second and third experiments, and various countermeasures are evaluated. While the CGAPP platform exhibits certain vulnerabilities to data injection attacks, straightforward countermeasures can alleviate them. Nevertheless, the system’s performance experiences a notable impact in the event of a data perturbation attack, and the countermeasures investigated are ineffective in addressing this issue.

SISTEM INFORMASI AKADEMIK SISWA BERBASIS WEBSITE PADA SMAN 1 NGABANG

Information system for Sman 1 Ngabang to process academic of students this web-based and mobile web system can help schools in learning features. The web- based is well designed using the PHP programming language and for the database using MySQL and phpMyAdmin. For testing using the white box method. The method used in software development is Rapid Application Development. The results obtained are Sman 1 Ngabang uses a web-based to obtain information system data in the form of reports. Admin also has full data access to add, update, and delete existing data in the database.

A novel privacy preserving blockchain based secure storage framework for electronic health records

Despite the continuous improvement of healthcare technology, implementing a patient health record system still has a significant challenge due to its sensitivity. Apart from storing and sharing patient data across various healthcare facilities, the system also has a tendency to distribute the data among various providers. Most patient health record systems (EHRs) use a centralized management structure to store and transmit patient information. This approach can create privacy concerns as it transfers sensitive information to a healthcare facility. Digitized Electronic Health Record EHRs that stores the patient’s medical history gathers from various IoT devices or from the centralized or decentralized database. These database records are often shared among various healthcare organizations. Most patient health record systems typically adopt a centralized approach and store their patient information in the cloud. This approach can raise privacy concerns due to the amount of sensitive information that is stored in the cloud. This paper proposes blockchain framework for secured electronic health records sharing and provides full data access to the patient, who is the owner of his health records. With this novel approach, our framework applies the Ethereum blockchain smart contracts, the Inter-Planetary File System (IPFS) as an off-chain storage system and key management. Blockchain based encryption creates an effective approach while sharing health records in distributed framework. Proposed framework is more secure than previously available framework. EHR’s will not be accessible from unauthorized users.

Surveillance of seasonal respiratory viruses among Chilean patients during the COVID-19 pandemic.

Surveillance of seasonal respiratory viruses among Chilean patients during the COVID-19 pandemic.

NCATS Inxight Drugs: a comprehensive and curated portal for translational research.

The United States has a complex regulatory scheme for marketing drugs. Understanding drug regulatory status is a daunting task that requires integrating data from many sources from the United States Food and Drug Administration (FDA), US government publications, and other processes related to drug development. At NCATS, we created Inxight Drugs (https://drugs.ncats.io), a web resource that attempts to address this challenge in a systematic manner. NCATS Inxight Drugs incorporates and unifies a wealth of data, including those supplied by the FDA and from independent public sources. The database offers a substantial amount of manually curated literature data unavailable from other sources. Currently, the database contains 125 036 product ingredients, including 2566 US approved drugs, 6242 marketed drugs, and 9684 investigational drugs. All substances are rigorously defined according to the ISO 11238 standard to comply with existing regulatory standards for unique drug substance identification. A special emphasis was placed on capturing manually curated and referenced data on treatment modalities and semantic relationships between substances. A supplementary resource 'Novel FDA Drug Approvals' features regulatory details of newly approved FDA drugs. The database is regularly updated using NCATS Stitcher data integration tool that automates data aggregation and supports full data access through a RESTful API.

Abstract 242: Transferring diagnostic and prognostic molecular models across technological platforms

Abstract The reproducibility of results obtained using RNA data across labs is a major hurdle in cancer research. Difference in library preparation methods and gene expression quantification platforms prevent the application of trained models to new data across labs. SpinAdapt is a novel unsupervised domain adaptation algorithm that enables the transfer of existing molecular models across labs and technological platforms, without requiring re-training or calibration of existing models for future prospective data. Furthermore, SpinAdapt uses summary statistics (independent latent space representations) to calculate data corrections, rather than requiring full data access. This allows for transfer of molecular models across sequencing platforms and between labs without loss of data ownership or compromise of data privacy. To evaluate SpinAdapt, we performed two sets of experiments: A) We transferred molecular tumor subtype classifiers across four pairs of publicly available cancer datasets (bladder, breast, colorectal, pancreatic), covering 4,076 samples across 18 different tumor subtypes and three technological platforms (RNASeq, Affymetrix U133plus2, and HE1ST). For each pair of datasets we trained a subtype classifier on one dataset (target) according to well-accepted subtyping annotations (Zea Tan et al. 2019; Prat et al. 2012; Guinney et al. 2015; Bailey et al. 2016), and then evaluated the classifier accuracy on the other dataset (source). For each tumor subtype, we quantified the classification performance using mean AUC score across random subsets of the source dataset, where each subset was corrected using SpinAdapt. We aggregated performance across all subtypes and report the average mean AUC score for each cancer type: bladder 0.95, breast 0.98, colorectal 0.98, pancreatic 0.96; demonstrating high accuracy on all diagnostic tasks. B) To demonstrate the transferability of prognostic models, we trained five Cox survival models on five target cancer datasets respectively (breast, lung, colorectal, liver, pancreatic) ranging from 186 to 2,919 RNASeq samples. We used SpinAdapt to adapt five source cancer datasets to the target datasets, ranging from 226 to 1,038 samples across different platforms (RNASeq, Affymetrix U133Plus2 and HG-U133A, Illumina HumanHT-12v4). For every cancer type, we trained a Cox model on the target dataset, and measured its performance by predicting survival risk on the corresponding adapted source dataset. We show high survival prediction accuracy for all datasets (Log-rank P-values and c-index): lung [1e-6, 01.661], breast [5e-5, 0.626], liver [1e-4, 0.708], pancreatic [2e-4, 0.629], colorectal [9e-4, 0.661]. SpinAdapt transferred diagnostic and prognostic models over 14 cancer datasets covering 7,146 samples across six different cancer types and various platforms (RNASeq, microarray), while maintaining model accuracy and statistical significance. Citation Format: Talal Ahmed, Stephane Wenric, Mark Carty, Rafael Pelossof. Transferring diagnostic and prognostic molecular models across technological platforms [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 242.

Enabling Efficient and Geometric Range Query With Access Control Over Encrypted Spatial Data

As a basic query function, range query has been exploited in many scenarios such as SQL retrieves, location-based services, and computational geometry. Meanwhile, with explosive growth of data volume, users are increasingly inclining to store data on the cloud for saving local storage and computational cost. However, a long-standing problem is that the user’s data may be completely revealed to the cloud server because it has full data access right. To cope with this problem, a frequently-used method is to encrypt raw data before outsourcing them, but the availability and operability of data will be reduced significantly. In this paper, we propose an efficient and geometric range query scheme (EGRQ) supporting searching and data access control over encrypted spatial data. We employ secure KNN computation, polynomial fitting technique, and order-preserving encryption to achieve secure, efficient, and accurate geometric range query over cloud data. Then, we propose a novel spatial data access control strategy to refine user’s rights in our EGRQ. To improve the efficiency, R-tree is adopted to reduce the searching space and matching times in whole search process. Finally, we theoretically prove the security of our proposed scheme in terms of confidentiality of spatial data, privacy protection of index and trapdoor, and the unlinkability of trapdoors. In addition, extensive experiments demonstrate the high efficiency of our proposed model compared with existing schemes.

BC Data ScoutTM: A New Tool To Investigate Datasets For Health Research

IntroductionBC’s Ministry of Health (MOH) maintains many administrative databases with rich information and analytical potential. Researchers are keen to use these data for both discovery and applied research. Historically, limited views of data availability and populations therein have supported study feasibility. Therefore, we developed BC Data ScoutTM, a cohort browser.
 Objectives and ApproachWe developed a cohort browser service to provide information to researchers planning a study using MOH data. The objective was to create a tool that is simple to use, provides quick results and is free to users to encourage its use. A better understanding of the data available can improve study quality and expand the user-base by giving researchers access to information not previously available during the planning stages. The tool will be evaluated by examining the number of requests received and a user satisfaction survey. Plans are in place to expand into additional data sources and extend query sophistication.
 ResultsThe BC Data ScoutTM online tool provides cohort information in the form of highly aggregated, approximate results to researchers planning a study. It was developed by the MOH, the BC SUPPORT Unit and Population Data BC (PopData) and was launched in February 2018. The service is delivered by PopData. BC Data ScoutTM offers province-wide information for query, is accessible to a wide group of eligible researchers, and has data availability from the year 2000 onwards. Four types of MOH data are available for query: hospital data; physician data; pharmaceutical data; and demographics. In addition to determining study feasibility, the aggregate reports also help to further refine a full data access request and provide enough information to complete and strengthen a funding application.
 Conclusion/ImplicationsBC Data ScoutTM will be beneficial for researchers planning to request data. This preliminary information may increase the chances of meaningful research studies to obtain funding, and the production of relevant, high-quality research results. BC will be among the first jurisdictions across Canada to offer this type of feasibility service.

Identification of Potential MicroRNA Biomarkers by Meta-analysis.

Meta-analysis statistically assesses the results (e.g., effect sizes)across independent studiesthat are conducted in accordancewith similar protocols and objectives. Current genomic meta-analysis studies do not perform extensive re-analysis on raw data because full data access would not be commonplace, although the best practice of open research for sharing well-formed data have been actively advocated. This chapter describes a simple and easy-to-follow method for conducting meta-analysis of multiple studies without using raw data. Examples for meta-analysis of microRNAs (miRNAs) are provided to illustrate the method. MiRNAs are potential biomarkers for early diagnosis and epigenetic monitoring of diseases. A number of miRNAs have been identified to be differentially expressed, i.e., overexpressed or underexpressed, under diseased states but only a small fraction would be highly effective biomarkers or therapeutic targets of diseases. The meta-analysis method as described in this chapter aims to identify the miRNAsthat are consistently found dysregulatedacross independent studies as biomarkers.

Should We Be Confident in Peer Effects Estimated From Social Network Crawls?

Research in social network analysis and statistical relational learning has produced a number of methods for learning relational models from large-scale network data. Unfortunately, these methods have been developed under the unrealistic assumption of full data access. In practice, however, the data are often collected by crawling the network, due to proprietary access, limited resources, and privacy concerns. While prior studies have examined the impact of network crawling on the structural characteristics of the resulting samples, this work presents the first empirical study designed to assess the impact of widely used network crawlers on the estimation of peer effects. Our experiments demonstrate that the estimates obtained from network samples collected by existing crawlers can be quite inaccurate, unless a significant portion of the network is crawled. Meanwhile, motivated by recent advances in partial network crawling, we develop crawl-aware relational methods that provide accurate estimates of peer effects with statistical guarantees from partial crawls.

Using Linked Open Data to Improve Data Reuse in Zooarchaeology

The inability of journals and books to accommodate data and to make it reusable has led to the gradual loss of vast amounts of information. The practice of disseminating selected sub-sets of data (usually in summary tables) permits only very limited types of reuse, and thus hampers scholarship. In recent years, largely in response to increasing government and institutional requirements for full data access, the scholarly community is giving data more attention, and solutions for data management are emerging. However, seeing data management primarily as a matter of compliance means that the research community faces continued data loss, as many datasets enter repositories without adequate description to enable their reuse. Furthermore, because many archaeologists do not yet have experience in data reuse, they lack understanding of what “good” data management means in terms of their own research practices. This paper discusses Linked Open Data (LOD) as an approach to improving data description, intelligibility and discoverability to facilitate reuse. I present examples of how annotating zooarchaeology datasets with LOD can facilitate data integration without forcing standardization. I conclude by recognizing that data sharing is not without its challenges. However, the research community’s careful attention and recognition of datasets as valuable scholarly outputs will go a long way toward ensuring that the products of our work are more widely useful.

The Hawaiian Freshwater Algal Database (HfwADB): a laboratory LIMS and online biodiversity resource

BackgroundBiodiversity databases serve the important role of highlighting species-level diversity from defined geographical regions. Databases that are specially designed to accommodate the types of data gathered during regional surveys are valuable in allowing full data access and display to researchers not directly involved with the project, while serving as a Laboratory Information Management System (LIMS). The Hawaiian Freshwater Algal Database, or HfwADB, was modified from the Hawaiian Algal Database to showcase non-marine algal specimens collected from the Hawaiian Archipelago by accommodating the additional level of organization required for samples including multiple species.DescriptionThe Hawaiian Freshwater Algal Database is a comprehensive and searchable database containing photographs and micrographs of samples and collection sites, geo-referenced collecting information, taxonomic data and standardized DNA sequence data. All data for individual samples are linked through unique 10-digit accession numbers (“Isolate Accession”), the first five of which correspond to the collection site (“Environmental Accession”). Users can search online for sample information by accession number, various levels of taxonomy, habitat or collection site. HfwADB is hosted at the University of Hawaii, and was made publicly accessible in October 2011. At the present time the database houses data for over 2,825 samples of non-marine algae from 1,786 collection sites from the Hawaiian Archipelago. These samples include cyanobacteria, red and green algae and diatoms, as well as lesser representation from some other algal lineages.ConclusionsHfwADB is a digital repository that acts as a Laboratory Information Management System for Hawaiian non-marine algal data. Users can interact with the repository through the web to view relevant habitat data (including geo-referenced collection locations) and download images of collection sites, specimen photographs and micrographs, and DNA sequences. It is publicly available at http://algae.manoa.hawaii.edu/hfwadb/.