scholarly journals Privacy Preserving for Sensitive Data using Data Masking Technique

2020 ◽  
Vol 8 (6) ◽  
pp. 5334-5337
Keyword(s):  
Information Hiding ◽  
Data Privacy ◽  
Banking Sector ◽  
Third Party ◽  
Sensitive Information ◽  
Sensitive Data ◽  
Memory Space ◽  
Space Organization ◽  
Data Masking ◽  
Using Data

In recent times, most the people are using internet where they are going to share sensitive information with other individual or with an organization like hospital, banking sector or business companies. So such huge amount of information will be stored on cloud. The attackers may try to hack the sensitive data and will try to misuse that data. So here the security for data comes first. There are numerous methods available to provide security for the data that is being shared among individuals or organizations. Most of the organizations take enough precautions to secure data that is shared with third party organizations. In recent times providing privacy for the sensitive data is high priority. The objective of this research is to discover the various data masking solutions for different applications for providing security to the data. Established data privacy method like AES or DES encryption technique proves to be proficient but time consuming. In order to avoid time consumption and to provide privacy for the data being shared, this paper proposes a information hiding method based on format-preserving encryption for sensitive data. This method will masquerade only sensitive data and make sure the encrypted data is still in the original format where it doesn’t consume much memory space. Organization like hospitals or banking sector or any business companies can use this format-preserving method to enhance the security of the data being shared. Tested the information on Spark illustrate that information hiding method based on format-preserving encryption can provide data privacy for sensitive data and preserve data format.

scholarly journals A Secured Public Auditing Protocol with Dynamic Structure for Cloud Data

2019 ◽  
Vol 9 (1S) ◽  
pp. 81-85
Keyword(s):  
Data Security ◽  
Information Hiding ◽  
Research Work ◽  
Dynamic Structure ◽  
Data Integrity ◽  
Third Party ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Sensitive Data ◽  
Cloud Data

At present Cloud computing is a very successful paradigm for data computing and storage. It Increases the concerns about data security and privacy in the cloud. Paper covers cloud security and privacy research, while focusing on the works that protect data confidentiality and privacy for sensitive data being stored and queried in the cloud. As Survey enlist all the research carried out related to data security and users privacy preserving techniques in detail. Data sharing can be achieved with sensitive information hiding with remote data integrity auditing, propose a new concept called identity based shared data integrity auditing with sensitive information hiding for secure cloud storage. Initially every data would be outsourced to the cloud only after authorized or activated by the proxy. The key would be generated to the file randomly by the key generation Centre. The transaction details such as key mismatch, file upload and download, hacking details would be shown to the proxy and cloud server. If the match occurs, automatically file would be recovered by the user even if hacker access or tamper the file. The main motive is to ensure that when the cloud properly stores the user’s sanitized data, the proof it generates can pass the verification of the third party auditor. And the paper provides various research work done in the field

scholarly journals Identification of Information Leakage and Guilt Agent by using MAC-IP Binding and Recursive Partitioning Algorithm to Modulate the Uncertainty in the Organization’s Network

2019 ◽  
Vol 9 (1) ◽  
pp. 4091-4096
Keyword(s):  
Decision Tree ◽  
Recursive Partitioning ◽  
Information Leakage ◽  
Third Party ◽  
Sensitive Information ◽  
Sensitive Data ◽  
The Third ◽  
Partitioning Algorithm ◽  
The Third Party ◽  
Modern Era

In this modern era, all organizations depend on internet and data so, maintaining of all data is done by the third party in large organizations. But in this present on-developing world, one have to share the data inside or outside the organization which incorporates the sensitive data of the venture moreover. Data of the organization have sensitive data which should not share with any others but unfortunately, that data was there in the third party hands so; we need to protect the data and also have to identify the guilt agent. For this, we propose a model that would evaluate and correctly identifies guilt agents, for which a recursive partitioning has been created which is a decision tree that spills data in to the sub partitions and does the easiest way to get alert and at least one specialist or it can autonomously accumulate by some different means. The main intention of the model is to secure sensitive information by recognizing the leakage and distinguish the guilt agent.

scholarly journals Data Privacy Preservation and Security Approaches for Sensitive Data in Big Data

2021 ◽  
Author(s):  
Rohit Ravindra Nikam ◽  
Rekha Shahapurkar
Keyword(s):  
Data Mining ◽  
Data Analytics ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Large Data ◽  
Research Area ◽  
Data Sets ◽  
Sensitive Information ◽  
Sensitive Data ◽  
Data Mining Techniques

Data mining is a technique that explores the necessary data is extracted from large data sets. Privacy protection of data mining is about hiding the sensitive information or identity of breach security or without losing data usability. Sensitive data contains confidential information about individuals, businesses, and governments who must not agree upon before sharing or publishing his privacy data. Conserving data mining privacy has become a critical research area. Various evaluation metrics such as performance in terms of time efficiency, data utility, and degree of complexity or resistance to data mining techniques are used to estimate the privacy preservation of data mining techniques. Social media and smart phones produce tons of data every minute. To decision making, the voluminous data produced from the different sources can be processed and analyzed. But data analytics are vulnerable to breaches of privacy. One of the data analytics frameworks is recommendation systems commonly used by e-commerce sites such as Amazon, Flip Kart to recommend items to customers based on their purchasing habits that lead to characterized. This paper presents various techniques of privacy conservation, such as data anonymization, data randomization, generalization, data permutation, etc. such techniques which existing researchers use. We also analyze the gap between various processes and privacy preservation methods and illustrate how to overcome such issues with new innovative methods. Finally, our research describes the outcome summary of the entire literature.

De-Identification of Health Data in Big Data using a Novel Bio-Inspired Apoptosis Algorithm

2015 ◽  
Vol 5 (3) ◽  
pp. 1-15
Author(s):  
Amine Rahmani ◽  
Abdelmalek Amine ◽  
Reda Mohamed Hamou
Keyword(s):  
Big Data ◽  
New Technologies ◽  
Sensitive Information ◽  
Sensitive Data ◽  
Privacy Concerns ◽  
Shared Data ◽  
Data Mining Algorithms ◽  
Identity Disclosure ◽  
Using Data ◽  
Mining Algorithms

In the last years, with the emergence of new technologies in the image of big data, the privacy concerns had grown widely. However, big data means the dematerialization of the data. The classical security solutions are no longer efficient in this case. Nowadays, sharing the data is much easier as well as saying hello. The amount of shared data over the web keeps growing from day to another which creates a wide gap between the purpose of sharing data and the fact that these last contain sensitive information. For that, the researches turned their attention to new issues and domains in order to minimize this gap. In other way, they intended to ensure a good utility of data by preserving its meaning while hiding sensitive information to prevent identity disclosure. Many techniques had been used for that. Some of it is mathematical and other ones using data mining algorithms. This paper deals with the problem of hiding sensitive data in shared structured medical data using a new bio-inspired algorithm from the natural phenomena of apoptosis cells in human body.

scholarly journals Cultural Variations in the Effect of Interview Privacy and the Need for Social Conformity on Reporting Sensitive Information

2015 ◽  
Vol 31 (4) ◽  
pp. 673-697 ◽  
Author(s):  
Zeina M. Mneimneh ◽  
Roger Tourangeau ◽  
Beth-Ellen Pennell ◽  
Steven G. Heeringa ◽  
Michael R. Elliott
Keyword(s):  
Potential Effect ◽  
Third Party ◽  
Sensitive Information ◽  
Social Conformity ◽  
Cultural Variations ◽  
Health Events ◽  
Neutral Information ◽  
World Mental Health Survey ◽  
Using Data ◽  
Privacy Setting

Abstract Privacy is an important feature of the interview interaction mainly due to its potential effect on reporting information, especially sensitive information. Here we examine the effect of third-party presence on reporting both sensitive and relatively neutral outcomes. We investigate whether the effect of third-party presence on reporting sensitive information is moderated by the respondent’s need for social conformity and the respondent’s country of residence. Three types of outcomes are investigated: behavioral, attitudinal, and relatively neutral health events. Using data from 22,070 interviews and nine countries in the cross-national World Mental Health Survey Initiative, we fit multilevel logistic regression to study reporting effects on questions about suicidal behavior and marital ratings, and contrast these with questions about having high blood pressure, asthma, or arthritis. We find that there is an effect of third-party presence on reporting sensitive information and no effect on reporting of neutral information. Further, the effect of the interview privacy setting on reporting sensitive information is moderated by the need for social conformity and the cultural setting.

scholarly journals Difference of Watermarking & Steganography

2019 ◽  
Vol 8 (4S3) ◽  
pp. 37-39
Keyword(s):  
Data Hiding ◽  
Information Hiding ◽  
Common Factors ◽  
Sensitive Information ◽  
Extraction Techniques ◽  
Sensitive Data ◽  
Internet Industry ◽  
Encryption And Decryption ◽  
Finger Printing ◽  
The Common

In the current world, the sensitive data are being transferred from source to destination in a much secured way in a common internet is inexorable. There are various technological aspects involved among the data world to protect the sensitive information or data hiding. Watermarking and Steganography are such important techniques which plays a prominent role in such data hiding. Earlier various techniques are been widely used like finger printing, Cryptography for encryption and decryption etc. But in the recent days the Digital Watermarking and Steganography are two range of techniques in such information hiding in a covered or secret way embedding to any host data which can be extracted with proper algorithms after the receiver receives the information. The combination of all these techniques can also bring a change in the internet industry. The information can be concealed and send across in a platform to the receiver with all these hidden techniques whereas the receiver of the data also need to know on the extraction techniques so that the information is been securely sent and received in a two-way communication. This paper deals about the comparing the common factors or attributes among the Watermarking and Steganography techniques.

scholarly journals The issues connected with the anonymization of medical data. Part 1. The introduction to the anonymization of medical data. Ensuring the protection of sensitive information with the use of such methods as f(a) and f(a,b)

2014 ◽  
Vol 8 (1) ◽  
pp. 13-21 ◽  
Author(s):  
ARKADIUSZ LIBER
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Differential Privacy ◽  
Medical Data ◽  
Medical Documentation ◽  
Sensitive Information ◽  
Sensitive Data ◽  
New Methods ◽  
Anonymized Data ◽  
Data Privacy Protection

Introduction: Medical documentation must be protected against damage or loss, in compliance with its integrity and credibility and the opportunity to a permanent access by the authorized staff and, finally, protected against the access of unauthorized persons. Anonymization is one of the methods to safeguard the data against the disclosure.Aim of the study: The study aims at the analysis of methods of anonymization, the analysis of methods of the protection of anonymized data and the study of a new security type of privacy enabling to control sensitive data by the entity which the data concerns.Material and methods: The analytical and algebraic methods were used.Results: The study ought to deliver the materials supporting the choice and analysis of the ways of the anonymization of medical data, and develop a new privacy protection solution enabling the control of sensitive data by entities whom this data concerns.Conclusions: In the paper, the analysis of solutions of data anonymizing used for medical data privacy protection was con-ducted. The methods, such as k-Anonymity, (X,y)- Anonymity, (a,k)- Anonymity, (k,e)-Anonymity, (X,y)-Privacy, LKC-Privacy, l-Diversity, (X,y)-Linkability, t-Closeness, Confidence Bounding and Personalized Privacy were described, explained and analyzed. The analysis of solutions to control sensitive data by their owners was also conducted. Apart from the existing methods of the anonymization, the analysis of methods of the anonimized data protection was conducted, in particular the methods of: d-Presence, e-Differential Privacy, (d,g)-Privacy, (a,b)-Distributing Privacy and protections against (c,t)-Isolation were analyzed. The author introduced a new solution of the controlled protection of privacy. The solution is based on marking a protected field and multi-key encryption of the sensitive value. The suggested way of fields marking is in accordance to the XML standard. For the encryption (n,p) different key cipher was selected. To decipher the content the p keys of n is used. The proposed solution enables to apply brand new methods for the control of privacy of disclosing sensitive data.

scholarly journals Smart-devices in Human Behavior Manipula-tion: Process diagram with exploratory assessment

2021 ◽  
Vol 1 (3) ◽  
pp. 54-58
Author(s):  
Md. Mojibur Rahman Redoy Akanda ◽  
Md. Alamgir Hossain
Keyword(s):  
Human Behavior ◽  
Health Management ◽  
Human Life ◽  
Personal Data ◽  
Third Party ◽  
Smart Devices ◽  
Smart Device ◽  
Sensitive Data ◽  
Care Health ◽  
Using Data

Smart devices have become an essential part of human life with a bunch of modern features and facilities. Even in health care, health management, education, and the science sector use intelligent devices for their convenience. With the assertion of its wellness, people forget its downside and treating smart devices as their primary need. Whereas smart devices are tracking and collecting all user movements, including interest, boredom, and daily activity. As the data remain store in vendors' servers, and lightweight smart devices follow weak security, so data leakage also makes the data available to unauthorized parties. This sensitive data uses by vendors and  third-party for business and various purposes to influence and manipulate human behavior by showing content mapping to the collected data. Because of the huge involvement of the user in smart-device, marketing strategy also changed a lot. Digital marketing has been  introduced and become a key to success for many businesses where a particular content/advertisement can be mapped to particular leads. The next move of a user on the internet is shaping by applying numerous strategies based on previously collected data. In the era of smart devices, our personal life and personal data are not remaining personal anymore. This paper illustrates the systematic process of collecting and using data for manipulating human behavior. The raise of human behavior manipulation has been explained and an exploratory survey is imputed to strongly support the research statement.

scholarly journals Privacy in Cloud-Based Computing

2020 ◽  
pp. 239-252
Author(s):  
Monjur Ahmed ◽  
Nurul I. Sarkar
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Data Privacy ◽  
Fog Computing ◽  
Edge Computing ◽  
Third Party ◽  
Research Topics ◽  
Sensitive Data ◽  
Privacy Issues ◽  
Third Party Ownership

Cloud computing, internet of things (IoT), edge computing, and fog computing are gaining attention as emerging research topics and computing approaches in recent years. These computing approaches are rather conceptual and contextual strategies rather than being computing technologies themselves, and in practice, they often overlap. For example, an IoT architecture may incorporate cloud computing and fog computing. Cloud computing is a significant concept in contemporary computing and being adopted in almost every means of computing. All computing architectures incorporating cloud computing are termed as cloud-based computing (CbC) in general. However, cloud computing itself is the basis of CbC because it significantly depends on resources that are remote, and the remote resources are often under third-party ownership where the privacy of sensitive data is a big concern. This chapter investigates various privacy issues associated with CbC. The data privacy issues and possible solutions within the context of cloud computing, IoT, edge computing, and fog computing are also explored.

Hardware-Based Security for Ensuring Data Privacy in the Cloud

2013 ◽  
pp. 147-170
Author(s):  
Wassim Itani ◽  
Ayman Kayssi ◽  
Ali Chehab
Keyword(s):  
Data Privacy ◽  
Sensitive Information ◽  
Software Protection ◽  
Legal Compliance ◽  
Sensitive Data ◽  
Customer Data ◽  
Privacy Concerns ◽  
Design Goal ◽  
Computing Services ◽  
Cloud Computing Services

In this chapter, the authors present a set of hardware-based security mechanisms for ensuring the privacy, integrity, and legal compliance of customer data as it is stored and processed in the cloud. The presented security system leverages the tamper-proof capabilities of cryptographic coprocessors to establish a secure execution domain in the computing cloud that is physically and logically protected from unauthorized access. The main design goal is to maximize users’ control in managing the various aspects related to the privacy of sensitive data by implementing user-configurable software protection and data privacy categorization mechanisms. Moreover, the proposed system provides a privacy feedback protocol to inform users of the different privacy operations applied on their data and to make them aware of any data leaks or risks that may jeopardize the confidentiality of their sensitive information. Providing a secure privacy feedback protocol increases the users’ trust in the cloud computing services, relieves their privacy concerns, and supports a set of accountable auditing services required to achieve legal compliance and certification.

Sign in / Sign up

Export Citation Format

Share Document