Protection to Personal Data Using Decentralizing Privacy of Blockchain.

2020 ◽  
pp. 173-194
Author(s):  
Vilas Baburao Khedekar ◽  
Shruti Sangmesh Hiremath ◽  
Prashant Madhav Sonawane ◽  
Dharmendra Singh Rajput
Keyword(s):  
New World ◽  
Personal Data ◽  
Large Data ◽  
Online Services ◽  
Online Systems

In today's world, we deal with various online services, where each person deals with various technologies. These technologies are made for people to make our access to the new world easily. There is a tremendous use of online applications, websites which require large storage. Large data is handled by the online systems. The collection of data in the whole world is about 20% in the last few years. The data is captured from the user, controlled by the systems, and operations are performed on data. It requires more system accuracy and protection to personal data. But the person does not know about the data, where and how it is used where it is stored or whether the data is handled by some organisations for their own use or data is been hacked by another person. This chapter explores protection of data using the decentralized privacy of blockchain.

scholarly journals Data Improvement Model Based on ECG Biometric for User Authentication and Identification

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2920 ◽  
Author(s):  
Alex Barros ◽  
Paulo Resque ◽  
João Almeida ◽  
Renato Mota ◽  
Helder Oliveira ◽  
...  
Keyword(s):  
User Authentication ◽  
Personal Data ◽  
Large Data ◽  
Real Data ◽  
Rejection Rate ◽  
Security Requirements ◽  
Biometric Identification ◽  
Data Set ◽  
Rapid Spread ◽  
Data Improvement

The rapid spread of wearable technologies has motivated the collection of a variety of signals, such as pulse rate, electrocardiogram (ECG), electroencephalogram (EEG), and others. As those devices are used to do so many tasks and store a significant amount of personal data, the concern of how our data can be exposed starts to gain attention as the wearable devices can become an attack vector or a security breach. In this context, biometric also has expanded its use to meet new security requirements of authentication demanded by online applications, and it has been used in identification systems by a large number of people. Existing works on ECG for user authentication do not consider a population size close to a real application. Finding real data that has a big number of people ECG’s data is a challenge. This work investigates a set of steps that can improve the results when working with a higher number of target classes in a biometric identification scenario. These steps, such as increasing the number of examples, removing outliers, and including a few additional features, are proven to increase the performance in a large data set. We propose a data improvement model for ECG biometric identification (user identification based on electrocardiogram—DETECT), which improves the performance of the biometric system considering a greater number of subjects, which is closer to a security system in the real world. The DETECT model increases precision from 78% to 92% within 1500 subjects, and from 90% to 95% within 100 subjects. Moreover, good False Rejection Rate (i.e., 0.064003) and False Acceptance Rate (i.e., 0.000033) were demonstrated. We designed our proposed method over PhysioNet Computing in Cardiology 2018 database.

scholarly journals TREND PREDICTION IN TEMPORAL BIPARTITE NETWORKS: THE CASE OF MOVIELENS, NETFLIX, AND DIGG

2013 ◽  
Vol 16 (04n05) ◽  
pp. 1350024 ◽  
Author(s):  
AN ZENG ◽  
STANISLAO GUALDI ◽  
MATÚŠ MEDO ◽  
YI-CHENG ZHANG
Keyword(s):  
Social Network ◽  
Prediction Performance ◽  
Prediction Methods ◽  
Bipartite Networks ◽  
Trend Prediction ◽  
Online Services ◽  
Online Systems ◽  
Near Future

Online systems, where users purchase or collect items of some kind, can be effectively represented by temporal bipartite networks where both nodes and links are added with time. We use this representation to predict which items might become popular in the near future. Various prediction methods are evaluated on three distinct datasets originating from popular online services (Movielens, Netflix, and Digg). We show that the prediction performance can be further enhanced if the user social network is known and centrality of individual users in this network is used to weight their actions.

scholarly journals Digital poverty: How online economy captures property inequality

2021 ◽  
Vol 49 (1) ◽  
pp. 138-164
Author(s):  
D.E. Konoplev ◽  
Keyword(s):  
Social Relations ◽  
Negative Impact ◽  
Personal Data ◽  
Loyalty Programs ◽  
Sociological Study ◽  
Online Services ◽  
Classical Communication ◽  
Digital Platforms ◽  
Economic Agents ◽  
The Rich

The article discusses the problem of digital poverty, arising when communication through digital platforms reduces the cost of the process of obtaining and exchanging information and replaces traditional economic processes. Using the example of the consumption of digital and online services, the author shows how digital communications can act as a marker for differentiating the behavior of the poor and the rich. Using cluster analysis and assessment of multicollinearity, the author interprets the data of a sociological study of five groups of respondents, indicating the factors of manifestation of digital poverty in the behavior of economic agents. The problem of the digital trace formed as a result of the automated data collection from users of online services is also considered. The author notes that consumers of digital services, in exchange for discounts, transfer their personal data to digital platforms that use the information received to stimulate further online consumption through new discounts and loyalty programs, which has a negative impact on offline consumption. The study also raises the issue of the accompanying digital poverty of economic externalities, identifies markers of property inequality in the digital economy, possible options for the development of the online economy against the background of the classical communication and social relations become luxurious. It also indicates the main scenarios for leveling the effects of digital poverty.

scholarly journals Human Performance in Google’s Two-factor Authentication Setup Process

2019 ◽  
Vol 63 (1) ◽  
pp. 2221-2225
Author(s):  
Shivam Pandey ◽  
Tewodros Taffese ◽  
Michelle Huang ◽  
Michael D. Byrne
Keyword(s):  
Cloud Computing ◽  
Social Networking ◽  
Human Performance ◽  
Personal Data ◽  
Usability Evaluation ◽  
Online Banking ◽  
Future Research ◽  
Diverse Populations ◽  
Online Services

Due to the proliferation of online services such as social networking, online banking, and cloud computing, more personal data are potentially exposed than ever before. Efforts such as two factor authentication (2FA) aim to make these services more secure; however, existing research efforts suggest this may come at the expense of usability. We conducted a usability evaluation of Google's 2FA setup process that confirms this concern, and extends previous efforts by identifying several problem areas and specific usability issues that affect human performance in 2FA setup processes. Future research should include more diverse populations but also continue efforts in improving the usability of 2FA setup processes. This will hopefully lead to increased adoption of 2FA systems.

What’s your anonymity worth? Establishing a marketplace for the valuation and control of individuals’ anonymity and personal data

2017 ◽  
Vol 19 (5) ◽  
pp. 353-366 ◽  
Author(s):  
Stephen Cory Robinson
Keyword(s):  
Personal Data ◽  
Conceptual Foundation ◽  
Content Type ◽  
Online Systems ◽  
Monetary Value ◽  
Proactive Management ◽  
Opt Out ◽  
The Government ◽  
New Perspective ◽  
And Control

Purpose The viability of online anonymity is questioned in today’s online environment where many technologies enable tracking and identification of individuals. In light of the shortcomings of the government, industry and consumers in protecting anonymity, it is clear that a new perspective for ensuring anonymity is needed. Where current stakeholders have failed to protect anonymity, some proponents argue that economic models exist for valuation of anonymity. By placing a monetary value on anonymity through Rawls’ concept of primary goods, it is possible to create a marketplace for anonymity, therefore allowing users full control of how their personal data is used. This paper aims to explore the creation of a data marketplace, offering users the possibility of engaging with companies and other entities to sell and auction personal data. Importantly, participation in a marketplace does not sacrifice one’s anonymity, as there are different levels of anonymity in online systems. Design/methodology/approach The paper uses a conceptual framework based on the abstractions of anonymity and data valuation. Findings The manuscript constructs a conceptual foundation for exploring the development and deployment of a personal data marketplace. By suggesting features allowing individuals’ control of their personal data, and properly establishing monetary valuation of one’s personal data, it is argued that individuals will undertake a more proactive management of personal data. Originality/value An overview of the available services and products offering increased anonymity is explored, in turn, illustrating the beginnings of a market response for anonymity as a valuable good. By placing a monetary value on individuals’ anonymity, it is reasoned that individuals will more consciously protect their anonymity in ways where legislation and other practices (i.e. privacy policies, marketing opt-out) have failed.

scholarly journals An extensible big data software architecture managing a research resource of real-world clinical radiology data linked to other health data from the whole Scottish population

GigaScience ◽  
2020 ◽  
Vol 9 (10) ◽  
Author(s):  
Thomas Nind ◽  
James Sutherland ◽  
Gordon McAllister ◽  
Douglas Hardy ◽  
Ally Hume ◽  
...  
Keyword(s):  
Real World ◽  
Clinical Decision Making ◽  
Clinical Care ◽  
Personal Data ◽  
Large Data ◽  
Clinical Decision ◽  
Learning Technologies ◽  
Training Algorithms ◽  
Clinical Images ◽  
Radiological Data

Abstract Aim To enable a world-leading research dataset of routinely collected clinical images linked to other routinely collected data from the whole Scottish national population. This includes more than 30 million different radiological examinations from a population of 5.4 million and >2 PB of data collected since 2010. Methods Scotland has a central archive of radiological data used to directly provide clinical care to patients. We have developed an architecture and platform to securely extract a copy of those data, link it to other clinical or social datasets, remove personal data to protect privacy, and make the resulting data available to researchers in a controlled Safe Haven environment. Results An extensive software platform has been developed to host, extract, and link data from cohorts to answer research questions. The platform has been tested on 5 different test cases and is currently being further enhanced to support 3 exemplar research projects. Conclusions The data available are from a range of radiological modalities and scanner types and were collected under different environmental conditions. These real-world, heterogenous data are valuable for training algorithms to support clinical decision making, especially for deep learning where large data volumes are required. The resource is now available for international research access. The platform and data can support new health research using artificial intelligence and machine learning technologies, as well as enabling discovery science.

scholarly journals Security Provisioning and Compression of Diverse Genomic Data based on Advanced Encryption Standard (AES) Algorithm

2021 ◽  
Vol 15 ◽  
pp. 104-112
Author(s):  
Raveendra Gudodagi ◽  
R. Venkata Siva Reddy
Keyword(s):  
Data Storage ◽  
Personal Data ◽  
Genomic Data ◽  
Large Data ◽  
Data File ◽  
Process Efficiency ◽  
Advanced Encryption Standard ◽  
Inappropriate Use ◽  
Data Preservation ◽  
Aes Algorithm

Compression of genomic data has gained enormous momentum in recent years because of advances in technology, exponentially growing health concerns, and government funding for research. Such advances have driven us to personalize public health and medical care. These pose a considerable challenge for ubiquitous computing in data storage. One of the main issues faced by genomic laboratories is the 'cost of storage' due to the large data file of the human genome (ranging from 30 GB to 200 GB). Data preservation is a set of actions meant to protect data from unauthorized access or changes. There are several methods used to protect data, and encryption is one of them. Protecting genomic data is a critical concern in genomics as it includes personal data. We suggest a secure encryption and decryption technique for diverse genomic data (FASTA / FASTQ format) in this article. Since we know the sequenced data is massive in bulk, the raw sequenced file is broken into sections and compressed. The Advanced Encryption Standard (AES) algorithm is used for encryption, and the Galois / Counter Mode (GCM) algorithm, is used to decode the encrypted data. This approach reduces the amount of storage space used for the data disc while preserving the data. This condition necessitates the use of a modern data compression strategy. That not only reduces storage but also improves process efficiency by using a k-th order Markov chain. In this regard, no efforts have been made to address this problem separately, from both the hardware and software realms. In this analysis, we support the need for a tailor-made hardware and software ecosystem that will take full advantage of the current stand-alone solutions. The paper discusses sequenced DNA, which may take the form of raw data obtained from sequencing. Inappropriate use of genomic data presents unique risks because it can be used to classify any individual; thus, the study focuses on the security provisioning and compression of diverse genomic data using the Advanced Encryption Standard (AES) Algorithm.

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