scholarly journals Security and Confidentiality of the Data using Block Level in Health Care System

2019 ◽  
Vol 8 (2) ◽  
pp. 2947-2951
Keyword(s):  
Rapid Development ◽  
Block Design ◽  
Personal Data ◽  
Third Party ◽  
Easy Access ◽  
Sensitive Information ◽  
User Privacy ◽  
Smart Healthcare ◽  
Block Level ◽  
Balanced Incomplete Block

Nowadays rapid development of cloud computing in smart healthcare system has significantly improved the quality of health. However, data security and user privacy are a major concern for smart healthcare systems. These days any kind of data can be used for malicious purposes. Many harmful entities constantly try to gain access to the personal data of internet users. This data includes sensitive information that doctors store of patients and is often stored using some kind of third party cloud providing service that is not very secure. To take care of this issue, in this paper, Symmetric Balanced Incomplete Block Design (SBIBD) is utilized for key Security so that unauthorized client can’t get access to the data easily. It also allows the patients immediate and easy access to the data using unique user ID. This system makes use of double encryption using Blowfish algorithm to ensure maximum security of data and the concept of block level is used where data is stored using multiple blocks.

scholarly journals Privacy in targeted advertising: A survey

2020 ◽  
Author(s):  
Imdad Ullah ◽  
Roksana Boreli ◽  
Salil S. Kanhere
Keyword(s):  
Private Information ◽  
Personal Data ◽  
Third Party ◽  
User Privacy ◽  
Research Issues ◽  
Privacy Concerns ◽  
Targeted Advertising ◽  
Privacy Threats ◽  
Measurement Analysis ◽  
Privacy Risks

Targeted advertising has transformed the marketing trend for any business by creating new opportunities for advertisers to reach prospective customers by delivering them personalised ads using an infrastructure of a variety of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process and trade a rich amount of user's personal data, which has prompted serious privacy concerns among individuals and organisations. This article presents a detailed survey of privacy risks including the information flow between advertising platform and ad/analytics networks, the profiling process, the advertising sources and criteria, the measurement analysis of targeted advertising based on user's interests and profiling context and ads delivery process in both in-app and in-browser targeted ads. We provide detailed discussion of challenges in preserving user privacy that includes privacy threats posed by the advertising and analytics companies, how private information is extracted and exchanged among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks, in addition to, overview data and tracking sharing technologies. Following, we present various techniques for preserving user privacy and a comprehensive analysis of various proposals founded on those techniques and compare them based on the underlying architectures, the privacy mechanisms and the deployment scenarios. Finally we discuss some potential research challenges and open research issues.<br>

scholarly journals Privacy in targeted advertising: A survey

2020 ◽  
Author(s):  
Imdad Ullah ◽  
Roksana Boreli ◽  
Salil S. Kanhere
Keyword(s):  
Private Information ◽  
Personal Data ◽  
Third Party ◽  
User Privacy ◽  
Research Issues ◽  
Privacy Concerns ◽  
Targeted Advertising ◽  
Privacy Threats ◽  
Measurement Analysis ◽  
Privacy Risks

Targeted advertising has transformed the marketing trend for any business by creating new opportunities for advertisers to reach prospective customers by delivering them personalised ads using an infrastructure of a variety of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process and trade a rich amount of user's personal data, which has prompted serious privacy concerns among individuals and organisations. This article presents a detailed survey of privacy risks including the information flow between advertising platform and ad/analytics networks, the profiling process, the advertising sources and criteria, the measurement analysis of targeted advertising based on user's interests and profiling context and ads delivery process in both in-app and in-browser targeted ads. We provide detailed discussion of challenges in preserving user privacy that includes privacy threats posed by the advertising and analytics companies, how private information is extracted and exchanged among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks, in addition to, overview data and tracking sharing technologies. Following, we present various techniques for preserving user privacy and a comprehensive analysis of various proposals founded on those techniques and compare them based on the underlying architectures, the privacy mechanisms and the deployment scenarios. Finally we discuss some potential research challenges and open research issues.<br>

scholarly journals Privacy-Constrained Biometric System for Non-Cooperative Users

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1033 ◽  
Author(s):  
Mohammad N. S. Jahromi ◽  
Pau Buch-Cardona ◽  
Egils Avots ◽  
Kamal Nasrollahi ◽  
Sergio Escalera ◽  
...  
Keyword(s):  
Recognition Performance ◽  
Personal Data ◽  
Sensitive Information ◽  
The European Union ◽  
Biometric System ◽  
User Privacy ◽  
Data Set ◽  
Spatial Features ◽  
The Face ◽  
Privacy Constraints

With the consolidation of the new data protection regulation paradigm for each individual within the European Union (EU), major biometric technologies are now confronted with many concerns related to user privacy in biometric deployments. When individual biometrics are disclosed, the sensitive information about his/her personal data such as financial or health are at high risk of being misused or compromised. This issue can be escalated considerably over scenarios of non-cooperative users, such as elderly people residing in care homes, with their inability to interact conveniently and securely with the biometric system. The primary goal of this study is to design a novel database to investigate the problem of automatic people recognition under privacy constraints. To do so, the collected data-set contains the subject’s hand and foot traits and excludes the face biometrics of individuals in order to protect their privacy. We carried out extensive simulations using different baseline methods, including deep learning. Simulation results show that, with the spatial features extracted from the subject sequence in both individual hand or foot videos, state-of-the-art deep models provide promising recognition performance.

Controlling privacy disclosure of third party applications in online social networks

2016 ◽  
Vol 12 (2) ◽  
pp. 215-241 ◽  
Author(s):  
Sarath Tomy ◽  
Eric Pardede
Keyword(s):  
Social Networks ◽  
Private Information ◽  
Online Social Networks ◽  
Structural Changes ◽  
Third Party ◽  
Sensitive Information ◽  
User Privacy ◽  
Complete Control ◽  
Content Type ◽  
User Awareness

Purpose The purpose of this paper is to analyse the problem of privacy disclosure of third party applications in online social networks (OSNs) through Facebook, investigate the limitations in the existing models to protect users privacy and propose a permission-based access control (PBAC) model, which gives users complete control over users’ data when accessing third party applications. Design/methodology/approach A practical model based on the defined permission policies is proposed to manage users information accessed by third party applications and improve user awareness in sharing sensitive information with them. This model is a combination of interfaces and internal mechanisms which can be adopted by any OSN having similar architecture to Facebook in managing third party applications, without much structural changes. The model implemented in Web interface connects with Facebook application programming interface and evaluates its efficacy using test cases. Findings The results show that the PBAC model can facilitate user awareness about privacy risks of data passed on to third party applications and allow users who are more concerned about their privacy from releasing such information to those applications. Research limitations/implications The study provides further research in protecting users’ privacy in OSNs and thus avoid the risks associated with that, thereby increasing users’ trust in using OSNs. Originality/value The research has proven to be useful in improving user awareness on the risk associated with sharing private information on OSNs, and the practically implemented PBAC model guarantees full user privacy from unwanted disclosure of personal information to third party applications.

scholarly journals Privacy in targeted advertising: A survey

2020 ◽  
Author(s):  
Imdad Ullah ◽  
Roksana Boreli ◽  
Salil S. Kanhere
Keyword(s):  
Private Information ◽  
Personal Data ◽  
Third Party ◽  
User Privacy ◽  
Research Issues ◽  
Privacy Concerns ◽  
Targeted Advertising ◽  
Privacy Threats ◽  
Measurement Analysis ◽  
Privacy Risks

Targeted advertising has transformed the marketing trend for any business by creating new opportunities for advertisers to reach prospective customers by delivering them personalised ads using an infrastructure of a variety of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process and trade a rich amount of user's personal data, which has prompted serious privacy concerns among individuals and organisations. This article presents a detailed survey of privacy risks including the information flow between advertising platform and ad/analytics networks, the profiling process, the advertising sources and criteria, the measurement analysis of targeted advertising based on user's interests and profiling context and ads delivery process in both in-app and in-browser targeted ads. We provide detailed discussion of challenges in preserving user privacy that includes privacy threats posed by the advertising and analytics companies, how private information is extracted and exchanged among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks, in addition to, overview data and tracking sharing technologies. Following, we present various techniques for preserving user privacy and a comprehensive analysis of various proposals founded on those techniques and compare them based on the underlying architectures, the privacy mechanisms and the deployment scenarios. Finally we discuss some potential research challenges and open research issues.<br>

Trend‐Free Nested Balanced Incomplete Block Designs and Designs for Diallel Cross Experiments

2007 ◽  
Vol 59 (3-4) ◽  
pp. 203-221
Author(s):  
Kishan Lal ◽  
Rajender Prasad ◽  
V. K. Gupta
Keyword(s):  
Block Design ◽  
Block Size ◽  
Diallel Cross ◽  
Block Designs ◽  
Incomplete Block ◽  
Diallel Crosses ◽  
Balanced Incomplete Block Designs ◽  
Block Level ◽  
Balanced Incomplete Block ◽  
Necessary And Sufficient

Abstract: Nested balanced incomplete block (NBIB) designs are useful when the experiments are conducted to deal with experimental situations when one nuisance factor is nested within the blocking factor. Similar to block designs, trend may exist in experimental units within sub‐blocks or within blocks in NBIB designs over time or space. A necessary and sufficient condition, for a nested block design to be trend‐free at sub‐block level, is derived. Families and catalogues of NBIB designs that can be converted into trend‐free NBIB designs at sub‐block and block levels have been obtained. A NBIB design with sub‐block size 2 has a one to one correspondence with designs for diallel crosses experiments. Therefore, optimal block designs for dialled cross experiments have been identified to check if these can be converted in to trend‐free optimal block designs for diallel cross experiments. A catalogue of such designs is also obtained. Trend‐free design is illustrated with example for a NBIB design and a design for diallel crosses experiments. AMS (2000) Subject Classification: 62K05, 62K10.

scholarly journals Innovative Commercial and Private Genetic Testing Raises Privacy and Confidentiality Concerns

2018 ◽  
pp. 31-33
Author(s):  
Syeda Masooma Zaidi
Keyword(s):  
Genetic Testing ◽  
Personal Information ◽  
Personal Data ◽  
Third Party ◽  
Sensitive Information ◽  
Test Results ◽  
Direct To Consumer ◽  
Client Data ◽  
The Right ◽  
Privacy And Confidentiality

Obtaining information about your genes can be as easy as swabbing your cheek for DNA testing. Companies that offer direct-to-consumer genetic testing with saliva have the authority to collect and share personal data as well as test results from their clients. However, patients want their personal information to be protected and although these companies ask for consent before sharing information with third-party sources, companies have the right to use client data to initiate research or improve their business. Genetic testing companies need to respect their clients and understand that they are paying for a service which deals with sensitive information that individuals may not want collected and stored.

Optimal Test Assembly in Practice

2013 ◽  
Vol 221 (3) ◽  
pp. 190-200 ◽  
Author(s):  
Jörg-Tobias Kuhn ◽  
Thomas Kiefer
Keyword(s):  
Particle Physics ◽  
Large Scale ◽  
Block Design ◽  
Linear Optimization ◽  
Mixed Integer ◽  
Optimal Test ◽  
Automated Test Assembly ◽  
Test Assembly ◽  
Block Level ◽  
Mixed Integer Linear Optimization

Several techniques have been developed in recent years to generate optimal large-scale assessments (LSAs) of student achievement. These techniques often represent a blend of procedures from such diverse fields as experimental design, combinatorial optimization, particle physics, or neural networks. However, despite the theoretical advances in the field, there still exists a surprising scarcity of well-documented test designs in which all factors that have guided design decisions are explicitly and clearly communicated. This paper therefore has two goals. First, a brief summary of relevant key terms, as well as experimental designs and automated test assembly routines in LSA, is given. Second, conceptual and methodological steps in designing the assessment of the Austrian educational standards in mathematics are described in detail. The test design was generated using a two-step procedure, starting at the item block level and continuing at the item level. Initially, a partially balanced incomplete item block design was generated using simulated annealing, whereas in a second step, items were assigned to the item blocks using mixed-integer linear optimization in combination with a shadow-test approach.

The Scoping Review of Smart Healthcare Applications in Medical Health (Preprint)

2020 ◽  
Author(s):  
Cheng Hang Wu ◽  
Ching Ju Chiu ◽  
Yen Ju Liou ◽  
Chun Ying Lee ◽  
Susan C. Hu
Keyword(s):  
Systematic Reviews ◽  
Mobile Health ◽  
Scoping Review ◽  
Search Strategy ◽  
Rapid Development ◽  
Related Research ◽  
Geographic Access ◽  
Smart Healthcare ◽  
Research Findings ◽  
Healthcare Interventions

BACKGROUND There is still no consensus on research terms for smart healthcare worldwide. The study conducted by Lewis 10 years ago showed extending geographic access was the major health purpose of health-related information communication technology (ICT), but today's situation may be different because of the rapid development of smart healthcare. Objective: The main aim of this study is to classify recent smart healthcare interventions. Therefore, this scoping review was conducted as a feasible tool for exploring this domain and summarizing related research findings. OBJECTIVE The main aim of this study is to classify recent smart healthcare interventions. Therefore, this scoping review was conducted as a feasible tool for exploring this domain and summarizing related research findings. METHODS The scoping review relies on the analysis of previous reviews of smart healthcare interventions assessed for their effectiveness in the framework of a systematic review and/or meta-analysis. The search strategy was based on the identification of smart healthcare interventions reported as the proposed keywords. In the analysis, the reviews published from January 2015 to December 2019 were included. RESULTS The number of publications for smart healthcare's systematic reviews has continued to grow in the past five years. The search strategy yielded 210 systematic reviews and/or meta-analyses addressed to target groups of interest. 68.5% of these publications used mobile health as a keyword. According to the classification by Lewis, 37.62% of the literature was applied to extend geographic access. According to the classification by the Joint Commission of Taiwan (JCT), 48.84% of smart healthcare was applied in clinical areas, and 60% of it was applied in outpatient medical services. CONCLUSIONS Smart healthcare interventions are being widely used in clinical settings and for disease management. The research of mobile health has received the most attention among smart healthcare interventions. The main purpose of mobile health was used to extend geographic access to increase medical accessibility in clinical areas. CLINICALTRIAL none

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