scholarly journals Detecting Data Integrity Failures

1997 ◽  
pp. 341-348
Author(s):  
W. List
Keyword(s):  
Data Integrity

Data Integrity Techniques in Cloud Computing: An Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 121 ◽  
Author(s):  
Neha Thakur ◽  
Aman Kumar Sharma
Keyword(s):  
Cloud Computing ◽  
Cloud Storage ◽  
Data Integrity ◽  
Advantages And Disadvantages ◽  
Security Models ◽  
Cloud Storage Service ◽  
Storage Service ◽  
Cloud Users ◽  
It Enterprises

Cloud computing has been envisioned as the definite and concerning solution to the rising storage costs of IT Enterprises. There are many cloud computing initiatives from IT giants such as Google, Amazon, Microsoft, IBM. Integrity monitoring is essential in cloud storage for the same reasons that data integrity is critical for any data centre. Data integrity is defined as the accuracy and consistency of stored data, in absence of any alteration to the data between two updates of a file or record.  In order to ensure the integrity and availability of data in Cloud and enforce the quality of cloud storage service, efficient methods that enable on-demand data correctness verification on behalf of cloud users have to be designed. To overcome data integrity problem, many techniques are proposed under different systems and security models. This paper will focus on some of the integrity proving techniques in detail along with their advantages and disadvantages.

Privacy Preserving Data Integrity Verification in Cloud

2014 ◽  
Vol 1 (2) ◽  
pp. 25-31
Author(s):  
T. Subha ◽  
◽  
S. Jayashri ◽  
Keyword(s):  
Data Integrity ◽  
Privacy Preserving ◽  
Integrity Verification

Towards guaranteed data-integrity: A method of preventing Questionable Research Practices

2018 ◽  
Author(s):  
Dick Bierman ◽  
Jacob Jolij
Keyword(s):  
Real Time ◽  
Open Data ◽  
Data Integrity ◽  
Drop Out ◽  
Research Practices ◽  
Major Aspect ◽  
Questionable Research Practices ◽  
Blockchain Technology ◽  
Integrity System ◽  
Secure Server

We have tested the feasibility of a method to prevent the occurrence of so-called Questionable Research Practices (QRP). A part from embedded pre-registration the major aspect of the system is real-time uploading of data on a secure server. We outline the method, discuss the drop-out treatment and compare it to the Born-open data method, and report on our preliminary experiences. We also discuss the extension of the data-integrity system from secure server to use of blockchain technology.

Need and Importance of Healthcare Data Integrity

2019 ◽  
Vol 11 (4) ◽  
pp. 854-859 ◽  
Author(s):  
Alka Agrawal ◽  
Nawaf Rasheed Alharbe
Keyword(s):  
Data Integrity ◽  
Healthcare Data

A feature-based hybrid recommender system for risk prediction : Machine learning approach (Preprint)

2020 ◽  
Author(s):  
Uzair Bhatti
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Predictive Accuracy ◽  
Correct Diagnosis ◽  
Recommendation Systems ◽  
Data Integrity ◽  
Machine Learning Algorithms ◽  
Patient Counseling ◽  
Hybrid Filtering ◽  
Novel Algorithm

BACKGROUND In the era of health informatics, exponential growth of information generated by health information systems and healthcare organizations demands expert and intelligent recommendation systems. It has become one of the most valuable tools as it reduces problems such as information overload while selecting and suggesting doctors, hospitals, medicine, diagnosis etc according to patients’ interests. OBJECTIVE Recommendation uses Hybrid Filtering as one of the most popular approaches, but the major limitations of this approach are selectivity and data integrity issues.Mostly existing recommendation systems & risk prediction algorithms focus on a single domain, on the other end cross-domain hybrid filtering is able to alleviate the degree of selectivity and data integrity problems to a better extent. METHODS We propose a novel algorithm for recommendation & predictive model using KNN algorithm with machine learning algorithms and artificial intelligence (AI). We find the factors that directly impact on diseases and propose an approach for predicting the correct diagnosis of different diseases. We have constructed a series of models with good reliability for predicting different surgery complications and identified several novel clinical associations. We proposed a novel algorithm pr-KNN to use KNN for prediction and recommendation of diseases RESULTS Beside that we compared the performance of our algorithm with other machine algorithms and found better performance of our algorithm, with predictive accuracy improving by +3.61%. CONCLUSIONS The potential to directly integrate these predictive tools into EHRs may enable personalized medicine and decision-making at the point of care for patient counseling and as a teaching tool. CLINICALTRIAL dataset for the trials of patient attached

Data Integrity

2017 ◽  
Author(s):  
Keith M. Martin
Keyword(s):  
Hash Function ◽  
Hash Functions ◽  
Data Integrity ◽  
Message Authentication ◽  
Authenticated Encryption ◽  
Authentication Codes ◽  
Basic Model ◽  
Function Design ◽  
Security Properties ◽  
Different Levels

This chapter discusses cryptographic mechanisms for providing data integrity. We begin by identifying different levels of data integrity that can be provided. We then look in detail at hash functions, explaining the different security properties that they have, as well as presenting several different applications of a hash function. We then look at hash function design and illustrate this by discussing the hash function SHA-3. Next, we discuss message authentication codes (MACs), presenting a basic model and discussing basic properties. We compare two different MAC constructions, CBC-MAC and HMAC. Finally, we consider different ways of using MACs together with encryption. We focus on authenticated encryption modes, and illustrate these by describing Galois Counter mode.

Ensuring input data integrity in a high-volume environment

1973 ◽  
Author(s):  
Robert F. Stover ◽  
S. Krishnaswamy
Keyword(s):  
Input Data ◽  
Data Integrity ◽  
High Volume
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